Pith. sign in

REVIEW 2 major objections 5 minor 1 cited by

About Time: Advances, Challenges, and Outlooks of Action Understanding

T0 review · 2 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This survey argues that the field of video action understanding is holistically organized by three temporal scopes—recognizing observed actions, predicting ongoing ones, and forecasting unseen ones—and that no prior survey has covered all…

desk verdict A comprehensive, genuinely useful survey whose promised three-scope temporal taxonomy does not cleanly contain text-to-video generation; fix that section and it is a solid reference. read the letter →

arxiv 2411.15106 v2 pith:SUEJCXKL submitted 2024-11-22 cs.CV cs.AIcs.LG

Alexandros Stergiou , Ronald Poppe This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords actionunderstandingrecognitionpredictionanticipationvideodatasetsmultimodalgenerationsurveytaxonomy
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This survey sets out to establish that the field of video action understanding, which has grown into dozens of task-specific research lines, is best understood as three temporal scopes: recognition of actions observed in full, prediction from partial observations of ongoing actions, and forecasting of actions not yet seen. The authors argue that prior surveys each cover one slice of the field—specific tasks, modalities, or scopes—and that no critical overview spanning all three has existed; this survey of 1,284 works is presented as filling that gap. The three-way division matters because each scope concentrates its own modeling problems: representing long-range dependencies for recognition, handling procedural proximity and partial evidence for prediction, and managing error accumulation and fixed anticipation intervals for forecasting. If the taxonomy holds, researchers and practitioners get a unified map of the field and of where the open problems concentrate.

What carries the argument

The organizing device is the temporal-scope taxonomy, a partitioning of tasks by how much of the action sequence the model can access: full observation (recognition), an observable prefix of an ongoing action (prediction), and only the current action while reasoning about a future, unobserved one (forecasting). Figure 2 formalizes this with a timeline in which an action of duration $\tau_1$ is only partially observable ($\tau_{1,\rho} < \tau_1$), a transition period $0 \leq \tau_{1\to2}$ separates actions, and the next action has duration $\tau_2$. The taxonomy is what carries the survey's argument: it is the grid into which the 1,284 surveyed works, the dataset tables, and the per-section challenges are placed, and it is the basis for the claim that no prior survey covers the field holistically.

What would settle it

A citation audit of the field would settle the completeness claim: take a representative sample of recent action-understanding publications and check whether each one can be assigned to one of the three temporal scopes with high agreement among independent coders, and whether the bibliometric counts behind the survey's research-trend figure (approximated from all works citing influential papers with at least 300 citations on Google Scholar) match exact citation data. If a substantial research line fits none of the three scopes, or if the approximated counts misstate the growth of major lines, the taxonomy and the claimed gap it fills would need revision.

Watch

Extended reading notes

Core claim

The central claim, stated in the survey's taxonomy section, is that the field of video action understanding can be holistically organized by where a model sits on the action timeline: recognition tasks use the full observation of an action of duration $\tau_1$, prediction tasks use only an observable prefix $\tau_{1,\rho}$ of an ongoing action, and forecasting tasks use the currently observed action to reason about a subsequent unobserved action after a transition interval. Around this division the survey arranges the field's main task families—temporal localization, spatiotemporal detection, repetition counting, and language-based recognition under the first scope; early action prediction, frame prediction, state-change tasks, and anomaly detection under the second; action anticipation and video generation under the third—along with the modeling approaches, datasets, and benchmarks each family relies on. The paper also uses the three-scope division to expose challenges specific to each temporal position, such as procedural proximity between similar actions under partial observation, the modality gap in video-language models, and the absence of standardized evaluation for generated video. The discovery, if accepted, is that action understanding is not a loose cluster of tasks but a field with a coherent temporal backbone.

Load-bearing premise

The survey's claim of holistic coverage rests on the assumption that the landmark papers it selects and the tasks it includes are truly representative of the field; the selection criteria are qualitative, since landmark papers are chosen by their relevance to the period's trends, so any bias in that selection would leave the claimed completeness unestablished.

Editorial extensions

If this is right

  • If the taxonomy is right, prior surveys are complementary slices of a single whole, and the field's history from early template matching to video-language foundation models reads as one continuous timeline rather than disconnected task communities.
  • Researchers entering action understanding can locate any task—temporal action localization, early action prediction, action anticipation, video generation—by its temporal scope and inherit the challenges and solution families the survey attaches to that scope.
  • The three scopes highlight shared weaknesses: real-time and multi-person settings are under-addressed in prediction and forecasting alike, and video generation lacks standardized benchmarks that test physical plausibility and prompt alignment.
  • Datasets and modeling approaches surveyed under each scope expose where benchmarks are missing, notably high-resolution video for frame prediction and unified evaluation protocols for generated video.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the temporal taxonomy is adopted as a field-wide map, an implicit prediction follows: models trained for tasks within one scope should transfer more readily to other tasks in the same scope than across scopes—a testable hypothesis the paper does not itself run.
  • The taxonomy's 'time as a stepping stone' framing suggests a natural extension beyond the paper: the same three-scope division could organize audio-only action understanding or robot policy learning, where partial observation and forecasting are equally central.
  • Because the paper's landmark selection is qualitative, its comprehensiveness claim could be independently stress-tested with an automated citation-network analysis that checks whether the three scopes cover the field's citation-dense research lines without residue.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 5 minor

Summary. This survey proposes a taxonomy of video action understanding organized by three temporal scopes: recognition of fully observed actions, prediction from partially observed ongoing actions, and forecasting of subsequent unobserved actions. Within this structure it reviews video modeling approaches, datasets, and a wide range of tasks, and it concludes with suggested research directions. The paper explicitly claims that prior surveys focus on specific aspects and that a holistic survey of action understanding is missing, which this work aims to fill using 1,284 references and a bibliometric trend analysis.

Significance. The survey's main strength is breadth: it assembles a very large literature across uni- and multimodal action understanding, provides useful dataset tables and task-by-task overviews, and offers a clear conceptual organization through the temporal-scope distinction. The paper does not rely on the authors' own methods for its central structure, so circularity risk is low. If the boundary issues in the taxonomy are resolved, this would be a valuable reference for the community. However, the completeness claim is currently stronger than the evidence: the forecasting scope contains tasks that do not satisfy its own definition, and the paper's selection and bibliometric methodology is not reproducible.

major comments (2)
  1. [Section 6.2 / Section 1.1] The forecasting scope is defined in Section 1.1 as using 'the currently observed action(s) to reason about future actions not yet observed,' but Section 6.2 places Video Generation under this scope and explicitly includes text-to-video generation, whose input is a textual prompt rather than an observed action, as well as unconditional generative models. The example in Figure 15 ('A vibrant underwater scene of a scuba diver exploring a shipwreck') illustrates that the conditioning input need not contain any observed action at all. This is an internal inconsistency: a substantial family of tasks placed in the forecasting scope does not meet the scope's defining condition. Since the central claim is that the three temporal scopes provide a complete organizational scheme, the forecasting definition should either be tightened to cover only action-conditioned future synthesis, with text-to-video and similar generation treated separately, or the completeness claim should be qualified accordingly.
  2. [Section 7 / Figure 17] The paper asserts holistic coverage ('this survey fills this void', Section 1.1) and reports 1,284 cited papers, but the selection protocol is not described. Figure 1 states that landmark papers are selected 'by their relevance to the period's trends,' and the Figure 17 counts are 'approximated from all works citing influential papers with >=300 citations' per the footnote, using Google Scholar. Without a reproducible search strategy, inclusion and exclusion criteria, and an estimate of the approximation error, the completeness claim is difficult to verify independently. Please add a methodology paragraph or supplementary protocol describing how the literature was searched and screened, and qualify the bibliometric numbers as indicative rather than exact.
minor comments (5)
  1. [Figure 13] Two subfigures are both labeled '(d)': the Visual Abductive Reasoning panel and the Video Alignment panel, which makes the subsequent (e) and (f) labels inconsistent. Please renumber the panels.
  2. [Section 3.3] The sentence 'We overview of benchmarks in three groups' is grammatically incomplete; it should read 'We overview benchmarks in three groups' or similar.
  3. [Table 3] The column header 'Video adaptations 1' contains a dangling superscript '1' that is not explained anywhere in the text or table caption.
  4. [Section 5.3.2] In the list of state-based tasks, the second entry says 'defining start-end times (action progress prediction)', which repeats the first entry; this should presumably refer to Event Boundary Detection.
  5. [Figure 3] The dataset list labels entry 89 as 'EK-101 (Damen et al 2022)', but the text and Table 2 consistently refer to this benchmark as EPIC-KITCHENS-100 (EK-100).

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the survey's temporal taxonomy is an organizational claim rather than a derived result, and the authors' self-citations are not load-bearing.

full rationale

This is an external-literature survey. Its central contribution, the three-scope temporal taxonomy (recognition, prediction, forecasting), is introduced in Section 1.1 as a definitional organizing scheme, not as a prediction derived from fitted parameters or from the authors' own prior results. The comprehensiveness claim rests on qualitative selection of landmark papers and approximated bibliometric counts, which are completeness and correctness risks but not circular reductions. The authors' own papers appear only as items in method overviews and Table 1; removing them would not alter the taxonomy or any conclusion. The potential misfiling of text-to-video and image-to-video generation under forecasting is an internal-consistency concern about the scope definitions, not a circular derivation, because no inference is forced by construction or by self-citation.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The survey's conclusions rest on editorial selection and on the temporal taxonomy, not on fitted parameters or new theoretical objects. No free parameters or invented entities are introduced.

assumptions (3)
  • domain assumption The selected landmark papers and tasks adequately represent the field of action understanding.
    Used in Section 1.1 and Figure 1; if the selection is biased, the survey's claimed comprehensiveness is unsupported.
  • domain assumption The three temporal scopes (recognition, prediction, forecasting) suffice to organize all relevant action understanding tasks.
    This is the paper's organizing principle, introduced in Section 1.1; tasks that do not fit the temporal framing may be underrepresented.
  • domain assumption Bibliometric counts from Google Scholar approximate research activity per task.
    Footnote in Section 7, used for Figure 17.

how reviews work

0 comments
Cite this review

Pith. "Pith review of About Time: Advances, Challenges, and Outlooks of Action Understanding." pith.science (2026). https://pith.science/paper/SUEJCXKL

@misc{pith2026241115106,
  author       = {Pith},
  title        = {Pith review of: About Time: Advances, Challenges, and Outlooks of Action Understanding},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SUEJCXKL}},
  note         = {Machine review of arXiv:2411.15106}
}
read the original abstract

We have witnessed impressive advances in video action understanding. Increased dataset sizes, variability, and computation availability have enabled leaps in performance and task diversification. Current systems can provide coarse- and fine-grained descriptions of video scenes, extract segments corresponding to queries, synthesize unobserved parts of videos, and predict context across multiple modalities. This survey comprehensively reviews advances in uni- and multi-modal action understanding across a range of tasks. We focus on prevalent challenges, overview widely adopted datasets, and survey seminal works with an emphasis on recent advances. We broadly distinguish between three temporal scopes: (1) recognition tasks of actions observed in full, (2) prediction tasks for ongoing partially observed actions, and (3) forecasting tasks for subsequent unobserved action(s). This division allows us to identify specific action modeling and video representation challenges. Finally, we outline future directions to address current shortcomings.

Figures

Figures reproduced from arXiv: 2411.15106 by the authors.

Figure 1
Figure 1. Action understanding historical overview. We present popular tasks over time. Landmark papers are selected by their relevance to the period’s trends. Most tasks remain popular today. action understanding. Shown in [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Action understanding tasks. The progress of the video is indicated by the top bar. From the currently performed action of total duration τ1, only the τ1,ρ < τ1 part is readily observable. After a transition period 0 ≤ τ1→2, another action is performed with duration τ2. Action recognition tasks consider full observations of the action at τ1. Action prediction uses only part τ1,ρ of the ongoing action. Action forecast… view at source ↗
Figure 3
Figure 3. Datasets compared by total dataset duration and primary modality. Circle sizes correspond to the (approximate) summed duration of all videos in the datasets. Recent datasets (i.e., > 80) have longer total running times and include additional modalities such as language or audio. automation in video collection has been achieved with the use of embeddings from vision encoders (Chen et al 2020a; Huang et al 2024e; Zhu … view at source ↗
Figures from the paper (14 more)
Figure 4
Figure 4. Figure 4: Redundancy reduction methods include (a) se￾lection of task-specific salient frames, (b) use of supple￾mentary modalities such as audio to preview relevant regions to sample from, (c) input permutations to com￾press irrelevant frames and segments, and (d) using embeddi…
Figure 5
Figure 5. Figure 5: Visualization of temporal-based tasks. (a) Temporal Action Localization (TAL) discovers the start and end times of individual actions. In contrast, (b) Spatio-Temporal Action Detection (STAD) is more complex as it requires temporally and spatially localizing actions wi…
Figure 6
Figure 6. Figure 6: VLM modality gap. Given video encoder EV and text encoder EL, video and text are embedded to z + v and z + l in a joint embedding space R Ω. VLM objectives align both z + v and z + l . Contrastive approaches (Chen et al 2020c; Oord et al 2018; Xu et al 2021) additional…
Figure 7
Figure 7. Figure 7: Video retrieval tasks. (a) Instance-based retrieval returns only a single video corresponding to a search query.(b) Semantics-based retrieval returns a ranking score corresponding to each video’s relevance to the search query. (c) Temporal Sentence Grounding (TSG) rece…
Figure 8
Figure 8. Figure 8: CRITIC metric for visual storytelling. Identities are obtained from character lists and descriptions fed to a co-referencing model. CRITIC (Han et al 2024) is calculated as the IoU between predicted and reference identities. relationships. Transformer approaches have i…
Figure 9
Figure 9. Figure 9: VideoQA approaches. The graph-based ap￾proach in (a) is based on the method from Park et al (2021a). The memory-based approach with a two-stage VLM in (b) is based on Yu et al (2023b). Videos sourced from Xiao et al (2021). Scene-graphs. Early VideoQA approaches were b…
Figure 10
Figure 10. Figure 10: 4D video understanding tasks. (a) 3D human pose and shape regression takes as input monocular videos and produces expressive 3D representations. (b) Human/hand-object interactions predict aspects of human￾object interactions, such as the contact area or the grasp. (c)…
Figure 11
Figure 11. Figure 11 [PITH_FULL_IMAGE:figures/full_fig_p022_11.png]
Figure 12
Figure 12. Figure 12: EAP methods grouped by approach. The three main clusters are colored. Smaller subgroups are de￾noted with dashed lines. The positioning of the works represents an abstract proximity of the research idea to other seminal works. approaches that generate unobserved frame…
Figure 13
Figure 13. Figure 13: Tasks relating to object and action state change. Each of the presented tasks can involve additional objectives. Another line of research works (Heidarivincheh et al 2016, 2018) is aimed at localizing the moments that actions are completed. The speed of action complet…
Figure 14
Figure 14. Figure 14: Forecasting future actions. Starting from the observed action, anticipation approaches infer the sequence of probable next actions. Predictions are shown in a narrative chart format similar to Randall (2009). Example selected from Grauman et al (2024). gressed future …
Figure 15
Figure 15. Figure 15: Video generation challenges. Failure cases in video generation can be attributed to (a) poor continuity between frames with appearance or motion changes that do not correspond to the intended concept, (b) failure to capture real-world physics, and (c) poor video and p…
Figure 16
Figure 16. Figure 16: Multi-task video generation model from Fu et al (2023). Based on a partial video and a text prompt used to condition a codebook, a text-condition video VQGAN generates missing frames. bine two Markov processes with the first (forward) cor￾rupting the input data to noi…
Figure 17
Figure 17. Figure 17: Number of action understanding papers per year. The research focus (bottom to top) includes video reduction approaches VRe, temporal tasks TS, vision and language methods V&L, multimodal models MM, early action prediction EAP, video frame prediction VFP, state-based t…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Bridging Perspectives: A Survey on Cross-view Collaborative Intelligence with Egocentric-Exocentric Vision

    cs.CV 2025-06 accept novelty 3.0 of 10

    A comprehensive review of cross-view video understanding that uses both first-person and third-person cameras, organized into a three-direction taxonomy with a dataset catalog and future research gaps.

Reference graph

Works this paper leans on

300 extracted references · 80 canonical work pages · cited by 1 Pith paper

  1. [1]

    write newline

    " write newline "" initialize.prev.this.status FUNCTION begin.bib " write newline preamble empty 'skip preamble write newline if " thebibliography " longest.label * " " * write newline " [1] #1 " write newline " url@samestyle " write newline " " write newline " [2] #2 " write newline " =0pt " write newline " " ALTinterwordstretchfactor * " " * write newli...

  2. [2]

    In: CVPR

    Aafaq N, Akhtar N, Liu W, Gilani SZ, Mian A (2019) Spatio-temporal dynamics and semantic attribute enriched visual encoding for video captioning. In: CVPR

    2019

  3. [3]

    In: CVPR

    Aakur SN, Sarkar S (2019) A perceptual prediction framework for self supervised event segmentation. In: CVPR

    2019

  4. [4]

    In: ICCV

    Abati D, Ben Yahia H, Nagel M, Habibian A (2023) Resq: Residual quantization for video perception. In: ICCV

    2023

  5. [5]

    In: ICCV

    Abdelsalam MA, Rangrej SB, Hadji I, Dvornik N, Derpanis KG, Fazly A (2023) Gepsan: Generative procedure step anticipation in cooking videos. In: ICCV

    2023

  6. [6]

    In: DAGM GCPR

    Abu Y, Ke Q, Schiele B, Gall J (2021) Long-term anticipation of activities with cycle consistency. In: DAGM GCPR

    2021

  7. [7]

    arXiv:160908675

    Abu-El-Haija S, Kothari N, Lee J, Natsev P, Toderici G, Varadarajan B, Vijayanarasimhan S (2016) Youtube-8m: A large-scale video classification benchmark. arXiv:160908675

    2016

  8. [8]

    In: CVPR

    Abu Farha Y, Richard A, Gall J (2018) When will you do what?-anticipating temporal occurrences of activities. In: CVPR

    2018

Show all 300 references
  1. [9]

    In: CVPR

    Acsintoae A, Florescu A, Georgescu MI, Mare T, Sumedrea P, Ionescu RT, Khan FS, Shah M (2022) Ubnormal: New benchmark for supervised open-set video anomaly detection. In: CVPR

    2022

  2. [10]

    In: ICMLw

    Adnan M, Ioannou Y, Tsai CY, Galloway A, Tizhoosh HR, Taylor GW (2022) Monitoring shortcut learning using mutual information. In: ICMLw

    2022

  3. [11]

    In: BMVC

    Agarwal N, Chen YT, Dariush B, Yang MH (2020) Unsupervised domain adaptation for spatio-temporal action localization. In: BMVC

    2020

  4. [12]

    Aggarwal JK, Cai Q (1999) Human motion analysis: A review. CVIU

    1999

  5. [13]

    In: Workshop on Motion of Non-rigid and Articulated Objects

    Aggarwal JK, Cai Q, Liao W, Sabata B (1994) Articulated and elastic non-rigid motion: A review. In: Workshop on Motion of Non-rigid and Articulated Objects

    1994

  6. [14]

    Aggarwal JK, Cai Q, Liao W, Sabata B (1998) Nonrigid motion analysis: Articulated and elastic motion. CVIU

    1998

  7. [15]

    Akbari H, Yuan L, Qian R, Chuang WH, Chang SF, Cui Y, Gong B (2021) Vatt: Transformers for multimodal self-supervised learning from raw video, audio and text. NeurIPS

    2021

  8. [16]

    241014340

    Aklilu J, Wang X, Yeung-Levy S (2024) Zero-shot action localization via the confidence of large vision-language models. 241014340

    2024

  9. [17]

    arXiv:240804810

    Al-Tahan H, Garrido Q, Balestriero R, Bouchacourt D, Hazirbas C, Ibrahim M (2024) Unibench: Visual reasoning requires rethinking vision-language beyond scaling. arXiv:240804810

    2024

  10. [18]

    In: CVPR

    Alayrac JB, Bojanowski P, Agrawal N, Sivic J, Laptev I, Lacoste-Julien S (2016) Unsupervised learning from narrated instruction videos. In: CVPR

    2016

  11. [19]

    In: ICCV

    Alayrac JB, Laptev I, Sivic J, Lacoste-Julien S (2017) Joint discovery of object states and manipulation actions. In: ICCV

    2017

  12. [20]

    Alayrac JB, Donahue J, Luc P, Miech A, Barr I, Hasson Y, Lenc K, Mensch A, Millican K, Reynolds M, et al (2022) Flamingo: a visual language model for few-shot learning. NeurIPS

    2022

  13. [21]

    IEEE TPAMI

    Alayrac JB, Miech A, Laptev I, Sivic J, et al (2024) Multi-task learning of object states and state-modifying actions from web videos. IEEE TPAMI

    2024

  14. [22]

    IEEE TPAMI

    Albanese M, Chellappa R, Cuntoor N, Moscato V, Picariello A, Subrahmanian V, Udrea O (2010) Pads: A probabilistic activity detection framework for video data. IEEE TPAMI

    2010

  15. [23]

    arXiv:200800744

    Albanie S, Liu Y, Nagrani A, Miech A, Coto E, Laptev I, Sukthankar R, Ghanem B, Zisserman A, Gabeur V, et al (2020) The end-of-end-to-end: A video understanding pentathlon challenge (2020). arXiv:200800744

    2020

  16. [24]

    Albu AB, Bergevin R, Quirion S (2008) Generic Temporal Segmentation of Cyclic Human Motion . PR

    2008

  17. [25]

    In: CVPR

    Ali MK, Kim D, Kim TH (2023) Task agnostic restoration of natural video dynamics. In: CVPR

    2023

  18. [26]

    Allen B, Curless B, Popovi \'c Z (2003) The space of human body shapes: reconstruction and parameterization from range scans. ACM TOG

    2003

  19. [27]

    In: SIGGRAPH

    Allen B, Curless B, Popovi \'c Z, Hertzmann A (2006) Learning a correlated model of identity and pose-dependent body shape variation for real-time synthesis. In: SIGGRAPH

    2006

  20. [28]

    In: WACVw

    AlMarri S, Zaheer MZ, Nandakumar K (2024) A multi-head approach with shuffled segments for weakly-supervised video anomaly detection. In: WACVw

    2024

  21. [29]

    arXiv:240512399

    Alonso E, Jelley A, Micheli V, Kanervisto A, Storkey A, Pearce T, Fleuret F (2024) Diffusion for world modeling: Visual details matter in atari. arXiv:240512399

    2024

  22. [30]

    In: ECCV

    Alper M, Averbuch-Elor H (2024) Emergent visual-semantic hierarchies in image-text representations. In: ECCV

    2024

  23. [31]

    In: ECCV

    Alwassel H, Heilbron FC, Escorcia V, Ghanem B (2018) Diagnosing error in temporal action detectors. In: ECCV

    2018

  24. [32]

    In: ICCV

    Alwassel H, Giancola S, Ghanem B (2021) Tsp: Temporally-sensitive pretraining of video encoders for localization tasks. In: ICCV

    2021

  25. [33]

    In: CVPR

    Amer MR, Todorovic S (2012) Sum-product networks for modeling activities with stochastic structure. In: CVPR

    2012

  26. [34]

    In: AAAI

    Amrani E, Ben-Ari R, Rotman D, Bronstein A (2021) Noise estimation using density estimation for self-supervised multimodal learning. In: AAAI

    2021

  27. [35]

    arXiv:230408477

    An J, Zhang S, Yang H, Gupta S, Huang JB, Luo J, Yin X (2023) Latent-shift: Latent diffusion with temporal shift for efficient text-to-video generation. arXiv:230408477

    2023

  28. [36]

    In: CVPR

    Anderson P, Wu Q, Teney D, Bruce J, Johnson M, S \"u nderhauf N, Reid I, Gould S, Van Den Hengel A (2018) Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments. In: CVPR

    2018

  29. [37]

    In: ICML

    Andrew G, Arora R, Bilmes J, Livescu K (2013) Deep canonical correlation analysis. In: ICML

    2013

  30. [38]

    In: SIGGRAPH

    Anguelov D, Srinivasan P, Koller D, Thrun S, Rodgers J, Davis J (2005) Scape: shape completion and animation of people. In: SIGGRAPH

    2005

  31. [39]

    In: ICCV

    Antol S, Agrawal A, Lu J, Mitchell M, Batra D, Zitnick CL, Parikh D (2015) Vqa: Visual question answering. In: ICCV

    2015

  32. [40]

    In: ECCV

    Arandjelovic R, Zisserman A (2018) Objects that sound. In: ECCV

    2018

  33. [41]

    In: ICCV

    Arnab A, Dehghani M, Heigold G, Sun C, Lu c i \'c M, Schmid C (2021 a ) Vivit: A video vision transformer. In: ICCV

    2021

  34. [42]

    In: CVPR

    Arnab A, Sun C, Schmid C (2021 b ) Unified graph structured models for video understanding. In: CVPR

    2021

  35. [43]

    In: CVPR

    Ashutosh K, Girdhar R, Torresani L, Grauman K (2023 a ) Hiervl: Learning hierarchical video-language embeddings. In: CVPR

    2023

  36. [44]

    Ashutosh K, Ramakrishnan SK, Afouras T, Grauman K (2023 b ) Video-mined task graphs for keystep recognition in instructional videos. NeurIPS

    2023

  37. [45]

    In: BMVC

    Astrid M, Zaheer MZ, Lee JY, Lee SI (2021 a ) Learning not to reconstruct anomalies. In: BMVC

    2021

  38. [46]

    In: ICCVw

    Astrid M, Zaheer MZ, Lee SI (2021 b ) Synthetic temporal anomaly guided end-to-end video anomaly detection. In: ICCVw

    2021

  39. [47]

    In: NeurIPS

    Aytar Y, Vondrick C, Torralba A (2016) Soundnet: Learning sound representations from unlabeled video. In: NeurIPS

    2016

  40. [48]

    In: ICPR

    Azy O, Ahuja N (2008) Segmentation of Periodically Moving Objects . In: ICPR

    2008

  41. [49]

    In: Interspeech

    Baade A, Peng P, Harwath D (2022) Mae-ast: Masked autoencoding audio spectrogram transformer. In: Interspeech

    2022

  42. [50]

    In: ICLR

    Babaeizadeh M, Finn C, Erhan D, Campbell RH, Levine S (2018) Stochastic variational video prediction. In: ICLR

    2018

  43. [51]

    Baccouche M, Mamalet F, Wolf C, Garcia C, Baskurt A (2011) Sequential deep learning for human action recognition. In: HBU

    2011

  44. [52]

    In: ICCVw

    Bacharidis K, Argyros A (2023) Repetition-aware Image Sequence Sampling for Recognizing Repetitive Human Actions . In: ICCVw

    2023

  45. [53]

    In: ECCV

    Bachmann R, Mizrahi D, Atanov A, Zamir A (2022) Multimae: Multi-modal multi-task masked autoencoders. In: ECCV

    2022

  46. [54]

    In: CVPR

    Badamdorj T, Rochan M, Wang Y, Cheng L (2022) Contrastive learning for unsupervised video highlight detection. In: CVPR

    2022

  47. [55]

    In: ICML

    Baevski A, Hsu WN, Xu Q, Babu A, Gu J, Auli M (2022) Data2vec: A general framework for self-supervised learning in speech, vision and language. In: ICML

    2022

  48. [56]

    In: CVPR

    Bagad P, Tapaswi M, Snoek CGM (2023) Test of time: Instilling video-language models with a sense of time. In: CVPR

    2023

  49. [57]

    In: CVPR

    Bai J, Gao K, Min S, Xia ST, Li Z, Liu W (2024 a ) Badclip: Trigger-aware prompt learning for backdoor attacks on clip. In: CVPR

    2024

  50. [58]

    In: CVPR

    Bai S, Ma B, Chang H, Huang R, Chen X (2022) Salient-to-broad transition for video person re-identification. In: CVPR

    2022

  51. [59]

    In: ECCV

    Bai Y, Wang Y, Tong Y, Yang Y, Liu Q, Liu J (2020) Boundary content graph neural network for temporal action proposal generation. In: ECCV

    2020

  52. [60]

    arXiv:241006456

    Bai Y, Zhou Y, Zhou J, Goh RSM, Ting DSW, Liu Y (2024 b ) From generalist to specialist: Adapting vision language models via task-specific visual instruction tuning. arXiv:241006456

    2024

  53. [61]

    In: ICCV

    Bain M, Nagrani A, Varol G, Zisserman A (2021) Frozen in time: A joint video and image encoder for end-to-end retrieval. In: ICCV

    2021

  54. [62]

    Baldassini FB, Shukor M, Cord M, Soulier L, Piwowarski B (2024) What makes multimodal in-context learning work? In: CVPRw

    2024

  55. [63]

    In: ICLR

    Ballas N, Yao L, Pal C, Courville A (2015) Delving deeper into convolutional networks for learning video representations. In: ICLR

    2015

  56. [64]

    In: CVPR

    Bandara WGC, Patel N, Gholami A, Nikkhah M, Agrawal M, Patel VM (2023) Adamae: Adaptive masking for efficient spatiotemporal learning with masked autoencoders. In: CVPR

    2023

  57. [65]

    Bansal H, Gopalakrishnan K, Dingliwal S, Bodapati S, Kirchhoff K, Roth D (2023) Rethinking the role of scale for in-context learning: An interpretability-based case study at 66 billion scale. In: ACL

    2023

  58. [66]

    In: ECCV

    Bansal S, Arora C, Jawahar C (2022) My view is the best view: Procedure learning from egocentric videos. In: ECCV

    2022

  59. [67]

    arXiv preprint arXiv:210608254

    Bao H, Dong L, Piao S, Wei F (2021) Beit: Bert pre-training of image transformers. arXiv preprint arXiv:210608254

    2021

  60. [68]

    Bao H, Wang W, Dong L, Liu Q, Mohammed OK, Aggarwal K, Som S, Piao S, Wei F (2022) Vlmo: Unified vision-language pre-training with mixture-of-modality-experts. NeurIPS

    2022

  61. [69]

    In: ICCV

    Baqu \'e P, Fleuret F, Fua P (2017) Deep occlusion reasoning for multi-camera multi-target detection. In: ICCV

    2017

  62. [70]

    In: ICLR

    Bardes A, Ponce J, LeCun Y (2021) Vicreg: Variance-invariance-covariance regularization for self-supervised learning. In: ICLR

    2021

  63. [71]

    arXiv:230712698

    Bardes A, Ponce J, LeCun Y (2023) Mc-jepa: A joint-embedding predictive architecture for self-supervised learning of motion and content features. arXiv:230712698

    2023

  64. [72]

    In: ICCVw

    Barekatain M, Mart \' M, Shih HF, Murray S, Nakayama K, Matsuo Y, Prendinger H (2017) Okutama-action: An aerial view video dataset for concurrent human action detection. In: ICCVw

    2017

  65. [73]

    In: ICCV

    Barnard K, Forsyth D (2001) Learning the semantics of words and pictures. In: ICCV

    2001

  66. [74]

    Barnard K, Duygulu P, Forsyth D, De Freitas N, Blei DM, Jordan MI (2003) Matching words and pictures. JMLR

    2003

  67. [75]

    Becattini F, Uricchio T, Seidenari L, Ballan L, Bimbo AD (2020) Am i done? predicting action progress in videos. TOMM

    2020

  68. [76]

    Beddiar DR, Nini B, Sabokrou M, Hadid A (2020) Vision-based human activity recognition: a survey. MTA

    2020

  69. [77]

    In: WACV

    Ben-Shabat Y, Yu X, Saleh F, Campbell D, Rodriguez-Opazo C, Li H, Gould S (2021) The ikea asm dataset: Understanding people assembling furniture through actions, objects and pose. In: WACV

    2021

  70. [78]

    BenAbdelkader C, Cutler RG, Davis LS (2004) Gait recognition using image self-similarity. EURASIP

    2004

  71. [79]

    In: CVPR

    Benaim S, Ephrat A, Lang O, Mosseri I, Freeman WT, Rubinstein M, Irani M, Dekel T (2020) Speednet: Learning the speediness in videos. In: CVPR

    2020

  72. [80]

    In: CVPR

    Benfold B, Reid I (2011) Stable multi-target tracking in real-time surveillance video. In: CVPR

    2011

  73. [81]

    IEEE TPAMI

    Bengio Y, Courville A, Vincent P (2013 a ) Representation learning: A review and new perspectives. IEEE TPAMI

    2013

  74. [82]

    arXiv:13083432

    Bengio Y, L \'e onard N, Courville A (2013 b ) Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv:13083432

    2013

  75. [83]

    Bertasius G, Wang H, Torresani L (2021) Is space-time attention all you need for video understanding? In: ICML

    2021

  76. [84]

    In: CVPR

    Bhatnagar BL, Xie X, Petrov IA, Sminchisescu C, Theobalt C, Pons-Moll G (2022) Behave: Dataset and method for tracking human object interactions. In: CVPR

    2022

  77. [85]

    In: CVPR

    Bilen H, Fernando B, Gavves E, Vedaldi A, Gould S (2016) Dynamic image networks for action recognition. In: CVPR

    2016

  78. [86]

    In: CVPR

    Black MJ, Patel P, Tesch J, Yang J (2023) Bedlam: A synthetic dataset of bodies exhibiting detailed lifelike animated motion. In: CVPR

    2023

  79. [87]

    In: ICCV

    Blank M, Gorelick L, Shechtman E, Irani M, Basri R (2005) Actions as space-time shapes. In: ICCV

    2005

  80. [88]

    In: CVPR

    Blattmann A, Rombach R, Ling H, Dockhorn T, Kim SW, Fidler S, Kreis K (2023) Align your latents: High-resolution video synthesis with latent diffusion models. In: CVPR

    2023

  81. [89]

    Bleeker M, Hendriksen M, Yates A, de Rijke M (2024) Demonstrating and reducing shortcuts in vision-language representation learning. TMLR

    2024

  82. [90]

    IEEE TPAMI

    Bobick AF, Davis JW (2001) The recognition of human movement using temporal templates. IEEE TPAMI

    2001

  83. [91]

    de Boer F, van Gemert JC, Dijkstra J, Pintea SL (2023) Is there progress in activity progress prediction? In: ICCVw

    2023

  84. [92]

    In: CVPR

    Bogo F, Romero J, Loper M, Black MJ (2014) Faust: Dataset and evaluation for 3d mesh registration. In: CVPR

    2014

  85. [93]

    In: CVPR

    Bogo F, Romero J, Pons-Moll G, Black MJ (2017) Dynamic faust: Registering human bodies in motion. In: CVPR

    2017

  86. [94]

    In: ACCV

    Bokhari SZ, Kitani KM (2017) Long-term activity forecasting using first-person vision. In: ACCV

    2017

  87. [95]

    In: CVPRw

    Bordt S, Upadhyay U, Akata Z, von Luxburg U (2023) The manifold hypothesis for gradient-based explanations. In: CVPRw

    2023

  88. [96]

    IEEE TPAMI

    Borji A, Itti L (2012) State-of-the-art in visual attention modeling. IEEE TPAMI

    2012

  89. [97]

    Online learning in neural networks

    Bottou L (1998) Online algorithms and stochastic approximations. Online learning in neural networks

    1998

  90. [98]

    IEEE TPAMI

    Briassouli A, Ahuja N (2007) Extraction and Analysis of Multiple Periodic Motions in Video Sequences . IEEE TPAMI

    2007

  91. [99]

    In: Eurographicsw 3D-OR

    Bronstein A, Bronstein M, Castellani U, Dubrovina A, Guibas L, Horaud R, Kimmel R, Knossow D, Von Lavante E, Mateus D, et al (2010) Shrec 2010: robust correspondence benchmark. In: Eurographicsw 3D-OR

    2010

  92. [100]

    Brooks T, Hellsten J, Aittala M, Wang TC, Aila T, Lehtinen J, Liu MY, Efros A, Karras T (2022) Generating long videos of dynamic scenes. NeurIPS

    2022

  93. [101]

    ://openai.com/research/video-generation-models-as-world-simulators

    Brooks T, Peebles B, Holmes C, DePue W, Guo Y, Jing L, Schnurr D, Taylor J, Luhman T, Luhman E, Ng C, Wang R, Ramesh A (2024) Video generation models as world simulators. ://openai.com/research/video-generation-models-as-world-simulators

    2024

  94. [102]

    Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A, et al (2020) Language models are few-shot learners. NeurIPS

    2020

  95. [103]

    Broxton M, Flynn J, Overbeck R, Erickson D, Hedman P, Duvall M, Dourgarian J, Busch J, Whalen M, Debevec P (2020) Immersive light field video with a layered mesh representation. ACM TOG

    2020

  96. [104]

    In: CVPRw

    Bugarin N, Bugaric J, Barusco M, Pezze DD, Susto GA (2024) Unveiling the anomalies in an ever-changing world: A benchmark for pixel-level anomaly detection in continual learning. In: CVPRw

    2024

  97. [105]

    Bulat A, Perez Rua JM, Sudhakaran S, Martinez B, Tzimiropoulos G (2021) Space-time mixing attention for video transformer. NeurIPS

    2021

  98. [106]

    Buxton H (2003) Learning and understanding dynamic scene activity: a review. IVC

    2003

  99. [107]

    In: CVPR

    Caba Heilbron F, Escorcia V, Ghanem B, Carlos Niebles J (2015) Activitynet: A large-scale video benchmark for human activity understanding. In: CVPR

    2015

  100. [108]

    IEEE TMM

    Cai D, Qian S, Fang Q, Hu J, Ding W, Xu C (2022 a ) Heterogeneous graph contrastive learning network for personalized micro-video recommendation. IEEE TMM

    2022

  101. [109]

    In: AAAI

    Cai Y, Li H, Hu JF, Zheng WS (2019) Action knowledge transfer for action prediction with partial videos. In: AAAI

    2019

  102. [110]

    In: ECCV

    Cai Z, Ren D, Zeng A, Lin Z, Yu T, Wang W, Fan X, Gao Y, Yu Y, Pan L, et al (2022 b ) Humman: Multi-modal 4d human dataset for versatile sensing and modeling. In: ECCV

    2022

  103. [111]

    Cerebral cortex

    Calvo-Merino B, Glaser DE, Gr \`e zes J, Passingham RE, Haggard P (2005) Action observation and acquired motor skills: an fmri study with expert dancers. Cerebral cortex

    2005

  104. [112]

    In: EMNLP

    Cao M, Chen L, Shou MZ, Zhang C, Zou Y (2021) On pursuit of designing multi-modal transformer for video grounding. In: EMNLP

    2021

  105. [113]

    In: CVPR

    Cao Y, Barrett D, Barbu A, Narayanaswamy S, Yu H, Michaux A, Lin Y, Dickinson S, Mark Siskind J, Wang S (2013) Recognize human activities from partially observed videos. In: CVPR

    2013

  106. [114]

    In: ECCV

    Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. In: ECCV

    2020

  107. [115]

    In: ICLR

    Carlini N, Terzis A (2022) Poisoning and backdooring contrastive learning. In: ICLR

    2022

  108. [116]

    Caron M, Misra I, Mairal J, Goyal P, Bojanowski P, Joulin A (2020) Unsupervised learning of visual features by contrasting cluster assignments. NeurIPS

    2020

  109. [117]

    In: ICCV

    Caron M, Touvron H, Misra I, J \'e gou H, Mairal J, Bojanowski P, Joulin A (2021) Emerging properties in self-supervised vision transformers. In: ICCV

    2021

  110. [118]

    In: CVPR

    Carreira J, Zisserman A (2017) Quo vadis, action recognition? a new model and the kinetics dataset. In: CVPR

    2017

  111. [119]

    arXiv:180801340

    Carreira J, Noland E, Banki-Horvath A, Hillier C, Zisserman A (2018) A short note about kinetics-600. arXiv:180801340

    2018

  112. [120]

    arXiv:190706987

    Carreira J, Noland E, Hillier C, Zisserman A (2019) A short note on the kinetics-700 human action dataset. arXiv:190706987

    2019

  113. [121]

    In: ICCV

    Castrejon L, Ballas N, Courville A (2019) Improved conditional vrnns for video prediction. In: ICCV

    2019

  114. [122]

    Cedras C, Shah M (1995) Motion-based recognition a survey. IVC

    1995

  115. [123]

    In: WACV

    Chaabane M, Trabelsi A, Blanchard N, Beveridge R (2020) Looking ahead: Anticipating pedestrians crossing with future frames prediction. In: WACV

    2020

  116. [124]

    Chaaraoui AA, Climent-P \'e rez P, Fl \'o rez-Revuelta F (2012) A review on vision techniques applied to human behaviour analysis for ambient-assisted living. ESWA

    2012

  117. [125]

    In: NeurIPS

    Chandrasegaran K, Gupta A, Hadzic LM, Kota T, He J, Eyzaguirre C, Durante Z, Li M, Wu J, Fei-Fei L (2024) Hourvideo: 1-hour video-language understanding. In: NeurIPS

    2024

  118. [126]

    In: CVPR

    Chang CY, Huang DA, Sui Y, Fei-Fei L, Niebles JC (2019) D3tw: Discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation. In: CVPR

    2019

  119. [127]

    Chang M, Prakash A, Gupta S (2024) Look ma, no hands! agent-environment factorization of egocentric videos. NeurIPS

    2024

  120. [128]

    In: NeurIPS

    Chang Z, Zhang X, Wang S, Ma S, Ye Y, Xinguang X, Gao W (2021) Mau: A motion-aware unit for video prediction and beyond. In: NeurIPS

    2021

  121. [129]

    In: CVPR

    Chang Z, Zhang X, Wang S, Ma S, Gao W (2022) Strpm: A spatiotemporal residual predictive model for high-resolution video prediction. In: CVPR

    2022

  122. [130]

    In: CVPR

    Chao YW, Vijayanarasimhan S, Seybold B, Ross DA, Deng J, Sukthankar R (2018) Rethinking the faster r-cnn architecture for temporal action localization. In: CVPR

    2018

  123. [131]

    In: CVPR

    Chao YW, Yang W, Xiang Y, Molchanov P, Handa A, Tremblay J, Narang YS, Van Wyk K, Iqbal U, Birchfield S, et al (2021) Dexycb: A benchmark for capturing hand grasping of objects. In: CVPR

    2021

  124. [132]

    In: ICCV

    Chatterjee M, Ahuja N, Cherian A (2021) A hierarchical variational neural uncertainty model for stochastic video prediction. In: ICCV

    2021

  125. [133]

    In: CVPR

    Chen C, Ashutosh K, Girdhar R, Harwath D, Grauman K (2024 a ) Soundingactions: Learning how actions sound from narrated egocentric videos. In: CVPR

    2024

  126. [134]

    Chen D, Dolan WB (2011) Collecting highly parallel data for paraphrase evaluation. In: ACL

    2011

  127. [135]

    In: AAAI

    Chen G, Zheng YD, Wang L, Lu T (2022 a ) Dcan: improving temporal action detection via dual context aggregation. In: AAAI

    2022

  128. [136]

    arXiv preprint arXiv:240309626

    Chen G, Huang Y, Xu J, Pei B, Chen Z, Li Z, Wang J, Li K, Lu T, Wang L (2024 b ) Video mamba suite: State space model as a versatile alternative for video understanding. arXiv preprint arXiv:240309626

    2024

  129. [137]

    In: ICASSP

    Chen H, Xie W, Vedaldi A, Zisserman A (2020 a ) Vggsound: A large-scale audio-visual dataset. In: ICASSP

    2020

  130. [138]

    arXiv:241111223

    Chen H, Huang Z, Hong Y, Wang Y, Lyu Z, Xu Z, Lan J, Gu Z (2024 c ) Efficient transfer learning for video-language foundation models. arXiv:241111223

    2024

  131. [139]

    In: EMNLP

    Chen J, Chen X, Ma L, Jie Z, Chua TS (2018 a ) Temporally grounding natural sentence in video. In: EMNLP

    2018

  132. [140]

    In: CVPR

    Chen L, Yan X, Xiao J, Zhang H, Pu S, Zhuang Y (2020 b ) Counterfactual samples synthesizing for robust visual question answering. In: CVPR

    2020

  133. [141]

    IEEE TCSVT

    Chen L, Lu J, Song Z, Zhou J (2022 b ) Ambiguousness-aware state evolution for action prediction. IEEE TCSVT

    2022

  134. [142]

    In: CVPR

    Chen M, Wei F, Li C, Cai D (2022 c ) Frame-wise action representations for long videos via sequence contrastive learning. In: CVPR

    2022

  135. [143]

    In: AAAI

    Chen M, Wen C, Zheng F, He F, Shao L (2022 d ) Vita: A multi-source vicinal transfer augmentation method for out-of-distribution generalization. In: AAAI

    2022

  136. [144]

    In: AAAI

    Chen P, Huang D, He D, Long X, Zeng R, Wen S, Tan M, Gan C (2021 a ) Rspnet: Relative speed perception for unsupervised video representation learning. In: AAAI

    2021

  137. [145]

    In: CVPR

    Chen S, Jiang YG (2021) Towards bridging event captioner and sentence localizer for weakly supervised dense event captioning. In: CVPR

    2021

  138. [146]

    In: ICMR

    Chen S, Chen J, Jin Q (2017 a ) Generating video descriptions with topic guidance. In: ICMR

    2017

  139. [147]

    In: ICCV

    Chen S, Sun P, Xie E, Ge C, Wu J, Ma L, Shen J, Luo P (2021 b ) Watch only once: An end-to-end video action detection framework. In: ICCV

    2021

  140. [148]

    Chen S, Li H, Wang Q, Zhao Z, Sun M, Zhu X, Liu J (2023 a ) Vast: A vision-audio-subtitle-text omni-modality foundation model and dataset. NeurIPS

    2023

  141. [149]

    In: ICLRw

    Chen S, Han Z, He B, Buckley M, Torr P, Tresp V, Gu J (2024 d ) Understanding and improving in-context learning on vision-language models. In: ICLRw

    2024

  142. [150]

    In: ICML

    Chen T, Kornblith S, Norouzi M, Hinton G (2020 c ) A simple framework for contrastive learning of visual representations. In: ICML

    2020

  143. [151]

    Chen T, Luo C, Li L (2021 c ) Intriguing properties of contrastive losses. NeurIPS

    2021

  144. [152]

    In: CVPR

    Chen TS, Siarohin A, Menapace W, Deyneka E, Chao Hw, Jeon BE, Fang Y, Lee HY, Ren J, Yang MH, et al (2024 e ) Panda-70m: Captioning 70m videos with multiple cross-modality teachers. In: CVPR

    2024

  145. [153]

    Chen X, Wang W, Wang J, Li W (2017 b ) Learning object-centric transformation for video prediction. In: MM

    2017

  146. [154]

    Chen Y, Kalantidis Y, Li J, Yan S, Feng J (2018 b ) A\^ 2-nets: Double attention networks. NeurIPS

    2018

  147. [155]

    In: ECCV

    Chen Y, Kalantidis Y, Li J, Yan S, Feng J (2018 c ) Multi-fiber networks for video recognition. In: ECCV

    2018

  148. [156]

    In: CVPR

    Chen Y, Fan H, Xu B, Yan Z, Kalantidis Y, Rohrbach M, Yan S, Feng J (2019) Drop an octave: Reducing spatial redundancy in convolutional neural networks with octave convolution. In: CVPR

    2019

  149. [157]

    In: AAAI

    Chen Y, Liu Z, Zhang B, Fok W, Qi X, Wu YC (2023 b ) Mgfn: Magnitude-contrastive glance-and-focus network for weakly-supervised video anomaly detection. In: AAAI

    2023

  150. [158]

    In: ECCV

    Cheng F, Bertasius G (2022) Tallformer: Temporal action localization with a long-memory transformer. In: ECCV

    2022

  151. [159]

    In: CVPR

    Cheng F, Xu M, Xiong Y, Chen H, Li X, Li W, Xia W (2022) Stochastic backpropagation: A memory efficient strategy for training video models. In: CVPR

    2022

  152. [160]

    In: CVPR

    Cheng F, Wang X, Lei J, Crandall D, Bansal M, Bertasius G (2023) Vindlu: A recipe for effective video-and-language pretraining. In: CVPR

    2023

  153. [161]

    In: CVPR

    Cheng S, Guo Z, Wu J, Fang K, Li P, Liu H, Liu Y (2024) Egothink: Evaluating first-person perspective thinking capability of vision-language models. In: CVPR

    2024

  154. [162]

    In: AAAI

    Cherian A, Hori C, Marks TK, Le Roux J (2022) (2.5+ 1) d spatio-temporal scene graphs for video question answering. In: AAAI

    2022

  155. [163]

    In: CVPR

    Chi Hg, Lee K, Agarwal N, Xu Y, Ramani K, Choi C (2023) Adamsformer for spatial action localization in the future. In: CVPR

    2023

  156. [164]

    Cho M, Kim T, Kim WJ, Cho S, Lee S (2022) Unsupervised video anomaly detection via normalizing flows with implicit latent features. PR

    2022

  157. [165]

    Choi J, Gao C, Messou JC, Huang JB (2019) Why can't i dance in the mall? learning to mitigate scene bias in action recognition. NeurIPS

    2019

  158. [166]

    In: ECCV

    Choi W, Savarese S (2012) A unified framework for multi-target tracking and collective activity recognition. In: ECCV

    2012

  159. [167]

    Nature Communications

    Chong E, Clark-Whitney E, Southerland A, Stubbs E, Miller C, Ajodan EL, Silverman MR, Lord C, Rozga A, Jones RM, Rehg JM (2020 a ) Detection of eye contact with deep neural networks is as accurate as human experts. Nature Communications

    2020

  160. [168]

    In: CVPR

    Chong E, Wang Y, Ruiz N, Rehg JM (2020 b ) Detecting attended visual targets in video. In: CVPR

    2020

  161. [169]

    Chu WH, Ke L, Fragkiadaki K (2024) Dreamscene4d: Dynamic multi-object scene generation from monocular videos. NeurIPS

    2024

  162. [170]

    In: CVPR

    Chun S, Oh SJ, De Rezende RS, Kalantidis Y, Larlus D (2021) Probabilistic embeddings for cross-modal retrieval. In: CVPR

    2021

  163. [171]

    In: ECCVw

    Chung J, Zisserman A (2016) Signs in time: Encoding human motion as a temporal image. In: ECCVw

    2016

  164. [172]

    In: ICCV

    Chung J, Wuu Ch, Yang Hr, Tai YW, Tang CK (2021) Haa500: Human-centric atomic action dataset with curated videos. In: ICCV

    2021

  165. [173]

    In: ICCV

    Cipolla R, Blake A (1990) The dynamic analysis of apparent contours. In: ICCV

    1990

  166. [174]

    arXiv:190706571

    Clark A, Donahue J, Simonyan K (2019) Adversarial video generation on complex datasets. arXiv:190706571

    2019

  167. [175]

    Cole E, Yang X, Wilber K, Mac Aodha O, Belongie S (2022) When does contrastive visual representation learning work? In: CVPR

    2022

  168. [176]

    In: CVPR

    Corona E, Pumarola A, Alenya G, Moreno-Noguer F, Rogez G (2020) Ganhand: Predicting human grasp affordances in multi-object scenes. In: CVPR

    2020

  169. [177]

    In: ECCV

    Coskun H, Zareian A, Moore JL, Tombari F, Wang C (2022) Goca: Guided online cluster assignment for self-supervised video representation learning. In: ECCV

    2022

  170. [178]

    In: ICCV

    Cui Y, Zeng C, Zhao X, Yang Y, Wu G, Wang L (2023) Sportsmot: A large multi-object tracking dataset in multiple sports scenes. In: ICCV

    2023

  171. [179]

    IEEE TPAMI

    Cutler R, Davis LS (2000) Robust Real-Time Periodic Motion Detection, Analysis, and Applications . IEEE TPAMI

    2000

  172. [180]

    Czolbe S, Krause O, Cox I, Igel C (2020) A loss function for generative neural networks based on watson’s perceptual model. NeurIPS

    2020

  173. [181]

    In: ICPR

    Da Costa VGT, Zara G, Rota P, Oliveira-Santos T, Sebe N, Murino V, Ricci E (2022) Unsupervised domain adaptation for video transformers in action recognition. In: ICPR

    2022

  174. [182]

    In: WACV

    Dai R, Das S, Minciullo L, Garattoni L, Francesca G, Bremond F (2021) Pdan: Pyramid dilated attention network for action detection. In: WACV

    2021

  175. [183]

    IEEE TPAMI

    Dai R, Das S, Sharma S, Minciullo L, Garattoni L, Bremond F, Francesca G (2022 a ) Toyota smarthome untrimmed: Real-world untrimmed videos for activity detection. IEEE TPAMI

    2022

  176. [184]

    arXiv:220506126,

    Dai Y, Tang D, Liu L, Tan M, Zhou C, Wang J, Feng Z, Zhang F, Hu X, Shi S (2022 b ) One model, multiple modalities: A sparsely activated approach for text, sound, image, video and code. arXiv:220506126,

    2022

  177. [185]

    In: BMVC

    Damen D, Leelasawassuk T, Haines O, Calway A, Mayol-Cuevas WW (2014) You-do, i-learn: Discovering task relevant objects and their modes of interaction from multi-user egocentric video. In: BMVC

    2014

  178. [186]

    Damen D, Leelasawassuk T, Mayol-Cuevas W (2016) You-do, i-learn: Egocentric unsupervised discovery of objects and their modes of interaction towards video-based guidance. CVIU

    2016

  179. [187]

    In: ECCV

    Damen D, Doughty H, Farinella GM, Fidler S, Furnari A, Kazakos E, Moltisanti D, Munro J, Perrett T, Price W, et al (2018) Scaling egocentric vision: The epic-kitchens dataset. In: ECCV

    2018

  180. [188]

    Damen D, Doughty H, Farinella GM, Furnari A, Kazakos E, Ma J, Moltisanti D, Munro J, Perrett T, Price W, et al (2022) Rescaling egocentric vision: Collection, pipeline and challenges for epic-kitchens-100. IJCV

    2022

  181. [189]

    In: IJCAI

    Dang LH, Le TM, Le V, Tran T (2021) Hierarchical object-oriented spatio-temporal reasoning for video question answering. In: IJCAI

    2021

  182. [190]

    Dao T, Fu D, Ermon S, Rudra A, R \'e C (2022) Flashattention: Fast and memory-efficient exact attention with io-awareness. NeurIPS

    2022

  183. [191]

    Dave I, Gupta R, Rizve MN, Shah M (2022) Tclr: Temporal contrastive learning for video representation. CVIU

    2022

  184. [192]

    In: ICCV

    Davtyan A, Sameni S, Favaro P (2023) Efficient video prediction via sparsely conditioned flow matching. In: ICCV

    2023

  185. [193]

    In: ECCV

    De Geest R, Gavves E, Ghodrati A, Li Z, Snoek CGM, Tuytelaars T (2016) Online action detection. In: ECCV

    2016

  186. [194]

    In: ECCV

    Delmas G, Weinzaepfel P, Lucas T, Moreno-Noguer F, Rogez G (2022) Posescript: 3d human poses from natural language. In: ECCV

    2022

  187. [195]

    In: CVPR

    Deng C, Chen S, Chen D, He Y, Wu Q (2021) Sketch, ground, and refine: Top-down dense video captioning. In: CVPR

    2021

  188. [196]

    In: ICML

    Denton E, Fergus R (2018) Stochastic video generation with a learned prior. In: ICML

    2018

  189. [197]

    IEEE TPAMI

    Dessalene E, Devaraj C, Maynord M, Ferm \"u ller C, Aloimonos Y (2021) Forecasting action through contact representations from first person video. IEEE TPAMI

    2021

  190. [198]

    In: WACV

    Destro M, Gygli M (2024) CycleCL: Self-supervised Learning for Periodic Videos . In: WACV

    2024

  191. [199]

    Dhariwal P, Nichol A (2021) Diffusion models beat gans on image synthesis. NeurIPS

    2021

  192. [200]

    In: ICCVw

    Dhiman A, Srinath R, Sarkar S, Boregowda LR, Babu RV (2023) Corf: Colorizing radiance fields using knowledge distillation. In: ICCVw

    2023

  193. [201]

    Dhiman C, Vishwakarma DK (2019) A review of state-of-the-art techniques for abnormal human activity recognition. EAAI

    2019

  194. [202]

    In: ECCV

    Diba A, Fayyaz M, Sharma V, Paluri M, Gall J, Stiefelhagen R, Van Gool L (2020) Large scale holistic video understanding. In: ECCV

    2020

  195. [203]

    In: ICCV

    Diba A, Sharma V, Safdari R, Lotfi D, Sarfraz S, Stiefelhagen R, Van Gool L (2021) Vi2clr: Video and image for visual contrastive learning of representation. In: ICCV

    2021

  196. [204]

    Artificial intelligence

    Dietterich TG, Lathrop RH, Lozano-P \'e rez T (1997) Solving the multiple instance problem with axis-parallel rectangles. Artificial intelligence

    1997

  197. [205]

    In: ECCV

    Diko A, Avola D, Prenkaj B, Fontana F, Cinque L (2024) Semantically guided representation learning for action anticipation. In: ECCV

    2024

  198. [206]

    IEEE TPAMI

    Ding G, Sener F, Yao A (2023) Temporal action segmentation: An analysis of modern techniques. IEEE TPAMI

    2023

  199. [207]

    IEEE TPAMI

    Ding K, Ma K, Wang S, Simoncelli EP (2020) Image quality assessment: Unifying structure and texture similarity. IEEE TPAMI

    2020

  200. [208]

    In: ECCV

    Ding S, Qian R, Xu H, Lin D, Xiong H (2024) Betrayed by attention: A simple yet effective approach for self-supervised video object segmentation. In: ECCV

    2024

  201. [209]

    In: VS-PETS

    Doll \'a r P, Rabaud V, Cottrell G, Belongie S (2005) Behavior recognition via sparse spatio-temporal features. In: VS-PETS

    2005

  202. [210]

    In: CVPR

    Donahue G, Elhamifar E (2024) Learning to predict activity progress by self-supervised video alignment. In: CVPR

    2024

  203. [211]

    In: CVPR

    Donahue J, Hendricks LA, Guadarrama S, Rohrbach M, Venugopalan S, Saenko K, Darrell T (2015) Long-term recurrent convolutional networks for visual recognition and description. In: CVPR

    2015

  204. [212]

    Dong H, Chharia A, Gou W, Vicente Carrasco F, De la Torre FD (2024) Hamba: Single-view 3d hand reconstruction with graph-guided bi-scanning mamba. NeurIPS

    2024

  205. [213]

    Dong J, Li X, Snoek CGM (2018) Predicting visual features from text for image and video caption retrieval. IEEE TM

    2018

  206. [214]

    In: CVPR

    Dorkenwald M, Milbich T, Blattmann A, Rombach R, Derpanis KG, Ommer B (2021) Stochastic image-to-video synthesis using cinns. In: CVPR

    2021

  207. [215]

    In: ICLR

    Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, et al (2020) An image is worth 16x16 words: Transformers for image recognition at scale. In: ICLR

    2020

  208. [216]

    In: CVPR

    Doughty H, Snoek CGM (2022) How do you do it? fine-grained action understanding with pseudo-adverbs. In: CVPR

    2022

  209. [217]

    In: CVPR

    Doughty H, Damen D, Mayol-Cuevas W (2018) Who's better? who's best? pairwise deep ranking for skill determination. In: CVPR

    2018

  210. [218]

    In: CVPR

    Doughty H, Laptev I, Mayol-Cuevas W, Damen D (2020) Action modifiers: Learning from adverbs in instructional videos. In: CVPR

    2020

  211. [219]

    Advances in Neural Information Processing Systems

    Du C, Li Y, Qiu Z, Xu C (2023) Stable diffusion is unstable. Advances in Neural Information Processing Systems

    2023

  212. [220]

    IEEE T-IP

    Du W, Wang Y, Qiao Y (2017) Recurrent spatial-temporal attention network for action recognition in videos. IEEE T-IP

    2017

  213. [221]

    In: ICCAIS

    Dubey S, Boragule A, Jeon M (2019) 3d resnet with ranking loss function for abnormal activity detection in videos. In: ICCAIS

    2019

  214. [222]

    Dvornik M, Hadji I, Derpanis KG, Garg A, Jepson A (2021) Drop-dtw: Aligning common signal between sequences while dropping outliers. NeurIPS

    2021

  215. [223]

    In: CVPRw

    Dwibedi D, Sermanet P, Tompson J (2018) Temporal reasoning in videos using convolutional gated recurrent units. In: CVPRw

    2018

  216. [224]

    In: CVPR

    Dwibedi D, Aytar Y, Tompson J, Sermanet P, Zisserman A (2020) Counting out time: Class agnostic video repetition counting in the wild. In: CVPR

    2020

  217. [225]

    arXiv:240717085

    Dwibedi D, Aytar Y, Tompson J, Zisserman A (2024) Ovr: A dataset for open vocabulary temporal repetition counting in videos. arXiv:240717085

    2024

  218. [226]

    In: CVPR

    Dwivedi SK, Sun Y, Patel P, Feng Y, Black MJ (2024) Tokenhmr: Advancing human mesh recovery with a tokenized pose representation. In: CVPR

    2024

  219. [227]

    Space and time in perception and action

    Eagleman DM (2010) How does the timing of neural signals map onto the timing of perception. Space and time in perception and action

    2010

  220. [228]

    Edwards M, Deng J, Xie X (2016) From pose to activity: Surveying datasets and introducing converse. CVIU

    2016

  221. [229]

    In: ICCV

    Efros A, Berg A, Mori G, Malik J (2003) Recognizing action at a distance. In: ICCV

    2003

  222. [230]

    In: ECCV

    Engel J, Sch \"o ps T, Cremers D (2014) Lsd-slam: Large-scale direct monocular slam. In: ECCV

    2014

  223. [231]

    In: CVPR

    Epstein D, Chen B, Vondrick C (2020) Oops! predicting unintentional action in video. In: CVPR

    2020

  224. [232]

    In: ICCV

    Epstein D, Wu J, Schmid C, Sun C (2021) Learning temporal dynamics from cycles in narrated video. In: ICCV

    2021

  225. [233]

    Escontrela A, Adeniji A, Yan W, Jain A, Peng XB, Goldberg K, Lee Y, Hafner D, Abbeel P (2023) Video prediction models as rewards for reinforcement learning. NeurIPS

    2023

  226. [234]

    arXiv:190712763

    Escorcia V, Soldan M, Sivic J, Ghanem B, Russell B (2019) Temporal localization of moments in video collections with natural language. arXiv:190712763

    2019

  227. [235]

    In: CVPR

    Esser P, Rombach R, Ommer B (2021) Taming transformers for high-resolution image synthesis. In: CVPR

    2021

  228. [236]

    In: NeurIPS

    Eyzaguirre C, Tang E, Buch S, Gaidon A, Wu J, Niebles JC (2024) Streaming detection of queried event start. In: NeurIPS

    2024

  229. [237]

    In: CVPR

    Fan C, Zhang X, Zhang S, Wang W, Zhang C, Huang H (2019) Heterogeneous memory enhanced multimodal attention model for video question answering. In: CVPR

    2019

  230. [238]

    In: ICCV

    Fan H, Xiong B, Mangalam K, Li Y, Yan Z, Malik J, Feichtenhofer C (2021) Multiscale vision transformers. In: ICCV

    2021

  231. [239]

    In: ICCV

    Fan K, Bai Z, Xiao T, Zietlow D, Horn M, Zhao Z, Simon-Gabriel CJ, Shou MZ, Locatello F, Schiele B, et al (2023) Unsupervised open-vocabulary object localization in videos. In: ICCV

    2023

  232. [240]

    In: CVPR

    Fan Z, Parelli M, Kadoglou ME, Chen X, Kocabas M, Black MJ, Hilliges O (2024) Hold: Category-agnostic 3d reconstruction of interacting hands and objects from video. In: CVPR

    2024

  233. [241]

    In: ICCV

    Fang H, Chen B, Wang X, Wang Z, Xia ST (2023) Gifd: A generative gradient inversion method with feature domain optimization. In: ICCV

    2023

  234. [242]

    In: CVPR

    Fathi A, Rehg JM (2013) Modeling actions through state changes. In: CVPR

    2013

  235. [243]

    In: ECCV

    Fathi A, Li Y, Rehg JM (2012) Learning to recognize daily actions using gaze. In: ECCV

    2012

  236. [244]

    In: WACV

    Faure GJ, Chen MH, Lai SH (2023) Holistic interaction transformer network for action detection. In: WACV

    2023

  237. [245]

    In: IJCAI

    Fayek HM, Kumar A (2020) Large scale audiovisual learning of sounds with weakly labeled data. In: IJCAI

    2020

  238. [246]

    In: CVPR

    Fei H, Wu S, Ji W, Zhang H, Chua TS (2024 a ) Dysen-vdm: Empowering dynamics-aware text-to-video diffusion with llms. In: CVPR

    2024

  239. [247]

    In: ICML

    Fei H, Wu S, Ji W, Zhang H, Zhang M, Lee ML, Hsu W (2024 b ) Video-of-thought: Step-by-step video reasoning from perception to cognition. In: ICML

    2024

  240. [248]

    In: CVPR

    Feichtenhofer C (2020) X3d: Expanding architectures for efficient video recognition. In: CVPR

    2020

  241. [249]

    In: CVPR

    Feichtenhofer C, Pinz A, Zisserman A (2016) Convolutional two-stream network fusion for video action recognition. In: CVPR

    2016

  242. [250]

    In: CVPR

    Feichtenhofer C, Pinz A, Wildes RP (2017) Spatiotemporal multiplier networks for video action recognition. In: CVPR

    2017

  243. [251]

    In: ICCV

    Feichtenhofer C, Fan H, Malik J, He K (2019) Slowfast networks for video recognition. In: ICCV

    2019

  244. [252]

    In: CVPR

    Feichtenhofer C, Fan H, Xiong B, Girshick R, He K (2021) A large-scale study on unsupervised spatiotemporal representation learning. In: CVPR

    2021

  245. [253]

    Feichtenhofer C, Li Y, He K, et al (2022) Masked autoencoders as spatiotemporal learners. NeurIPS

    2022

  246. [254]

    In: ICASSP

    Feng J, Erol MH, Chung JS, Senocak A (2024) From coarse to fine: Efficient training for audio spectrogram transformers. In: ICASSP

    2024

  247. [255]

    In: CVPR

    Feng JC, Hong FT, Zheng WS (2021 a ) Mist: Multiple instance self-training framework for video anomaly detection. In: CVPR

    2021

  248. [256]

    In: CVPR

    Feng R, Gao Y, Ma X, Tse THE, Chang HJ (2023) Mutual information-based temporal difference learning for human pose estimation in video. In: CVPR

    2023

  249. [257]

    In: CVPRw

    Feng Y, Jiang J, Huang Z, Qing Z, Wang X, Zhang S, Tang M, Gao Y (2021 b ) Relation modeling in spatio-temporal action localization. In: CVPRw

    2021

  250. [258]

    In: CVPR

    Fernando B, Herath S (2021) Anticipating human actions by correlating past with the future with jaccard similarity measures. In: CVPR

    2021

  251. [259]

    In: CVPR

    Fernando B, Gavves E, Oramas JM, Ghodrati A, Tuytelaars T (2015) Modeling video evolution for action recognition. In: CVPR

    2015

  252. [260]

    IEEE TPAMI

    Fernando B, Gavves E, Oramas J, Ghodrati A, Tuytelaars T (2016) Rank pooling for action recognition. IEEE TPAMI

    2016

  253. [261]

    In: CVPR

    Fernando B, Bilen H, Gavves E, Gould S (2017) Self-supervised video representation learning with odd-one-out networks. In: CVPR

    2017

  254. [262]

    Ferreira B, Ferreira PM, Pinheiro G, Figueiredo N, Carvalho F, Menezes P, Batista J (2021) Deep Learning Approaches for Workout Repetition Counting and Validation . PRL

    2021

  255. [263]

    In: ECCV

    Fiche G, Leglaive S, Alameda-Pineda X, Agudo A, Moreno-Noguer F (2024) Vq-hps: Human pose and shape estimation in a vector-quantized latent space. In: ECCV

    2024

  256. [264]

    In: CVPR

    Fieraru M, Zanfir M, Oneata E, Popa AI, Olaru V, Sminchisescu C (2020) Three-dimensional reconstruction of human interactions. In: CVPR

    2020

  257. [265]

    In: AAAI

    Fieraru M, Zanfir M, Oneata E, Popa AI, Olaru V, Sminchisescu C (2021) Learning complex 3d human self-contact. In: AAAI

    2021

  258. [266]

    Finn C, Goodfellow I, Levine S (2016) Unsupervised learning for physical interaction through video prediction. NeurIPS

    2016

  259. [267]

    In: ICCV

    Fioresi J, Dave IR, Shah M (2023) Ted-spad: Temporal distinctiveness for self-supervised privacy-preservation for video anomaly detection. In: ICCV

    2023

  260. [268]

    In: ICCV

    Flaborea A, Collorone L, Di Melendugno GMD, D'Arrigo S, Prenkaj B, Galasso F (2023) Multimodal motion conditioned diffusion model for skeleton-based video anomaly detection. In: ICCV

    2023

  261. [269]

    In: BMVC

    Flanagan K, Damen D, Wray M (2023) Learning temporal sentence grounding from narrated egovideos. In: BMVC

    2023

  262. [270]

    Fogassi L, Ferrari PF, Gesierich B, Rozzi S, Chersi F, Rizzolatti G (2005) Parietal lobe: from action organization to intention understanding. Science

    2005

  263. [271]

    In: ECCV

    Foo LG, Li T, Rahmani H, Ke Q, Liu J (2022) Era: Expert retrieval and assembly for early action prediction. In: ECCV

    2022

  264. [272]

    o rstner W, G \

    F \"o rstner W, G \"u lch E (1987) A fast operator for detection and precise location of distinct points, corners and centres of circular features. In: ICFPPD

    1987

  265. [273]

    In: CVPR

    Fouhey DF, Kuo Wc, Efros AA, Malik J (2018) From lifestyle vlogs to everyday interactions. In: CVPR

    2018

  266. [274]

    arXiv:170502082

    Fragkiadaki K, Huang J, Alemi A, Vijayanarasimhan S, Ricco S, Sukthankar R (2017) Motion prediction under multimodality with conditional stochastic networks. arXiv:170502082

    2017

  267. [275]

    In: ICML

    Franceschi JY, Delasalles E, Chen M, Lamprier S, Gallinari P (2020) Stochastic latent residual video prediction. In: ICML

    2020

  268. [276]

    arXiv:240521075

    Fu C, Dai Y, Luo Y, Li L, Ren S, Zhang R, Wang Z, Zhou C, Shen Y, Zhang M, et al (2024) Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis. arXiv:240521075

    2024

  269. [277]

    In: CVPR

    Fu Q, Liu X, Kitani KM (2022) Sequential decision-making for active object detection from hand. In: CVPR

    2022

  270. [278]

    arXiv:211112681

    Fu TJ, Li L, Gan Z, Lin K, Wang WY, Wang L, Liu Z (2021) Violet: End-to-end video-language transformers with masked visual-token modeling. arXiv:211112681

    2021

  271. [279]

    In: CVPR

    Fu TJ, Yu L, Zhang N, Fu CY, Su JC, Wang WY, Bell S (2023) Tell me what happened: Unifying text-guided video completion via multimodal masked video generation. In: CVPR

    2023

  272. [280]

    In: ICCV

    Furnari A, Farinella GM (2019) What would you expect? anticipating egocentric actions with rolling-unrolling lstms and modality attention. In: ICCV

    2019

  273. [281]

    In: ICPR

    Furnari A, Farinella GM (2022) Towards streaming egocentric action anticipation. In: ICPR

    2022

  274. [282]

    In: ECCVw

    Furnari A, Battiato S, Maria Farinella G (2018) Leveraging uncertainty to rethink loss functions and evaluation measures for egocentric action anticipation. In: ECCVw

    2018

  275. [283]

    In: ECCV

    Gabeur V, Sun C, Alahari K, Schmid C (2020) Multi-modal transformer for video retrieval. In: ECCV

    2020

  276. [284]

    IEEE TPAMI

    Gaidon A, Harchaoui Z, Schmid C (2013) Temporal localization of actions with actoms. IEEE TPAMI

    2013

  277. [285]

    Gallese V, Fadiga L, Fogassi L, Rizzolatti G (1996) Action recognition in the premotor cortex. Brain

    1996

  278. [286]

    In: ICCV

    Gammulle H, Denman S, Sridharan S, Fookes C (2019) Predicting the future: A jointly learnt model for action anticipation. In: ICCV

    2019

  279. [287]

    In: CVPR

    Gan Z, Gan C, He X, Pu Y, Tran K, Gao J, Carin L, Deng L (2017) Semantic compositional networks for visual captioning. In: CVPR

    2017

  280. [288]

    In: CVPR

    Gao D, Zhou L, Ji L, Zhu L, Yang Y, Shou MZ (2023) Mist: Multi-modal iterative spatial-temporal transformer for long-form video question answering. In: CVPR

    2023

  281. [289]

    In: ICCV

    Gao J, Sun C, Yang Z, Nevatia R (2017 a ) Tall: Temporal activity localization via language query. In: ICCV

    2017

  282. [290]

    arXiv:170704818

    Gao J, Yang Z, Nevatia R (2017 b ) Red: Reinforced encoder-decoder networks for action anticipation. arXiv:170704818

    2017

  283. [291]

    In: CVPR

    Gao J, Ge R, Chen K, Nevatia R (2018) Motion-appearance co-memory networks for video question answering. In: CVPR

    2018

  284. [292]

    In: CVPR

    Gao R, Oh TH, Grauman K, Torresani L (2020) Listen to look: Action recognition by previewing audio. In: CVPR

    2020

  285. [293]

    Gao Y, Liu J, Xu Z, Zhang J, Li K, Ji R, Shen C (2022 a ) Pyramidclip: Hierarchical feature alignment for vision-language model pretraining. NeurIPS

    2022

  286. [294]

    In: CVPR

    Gao Z, Tan C, Wu L, Li SZ (2022 b ) Simvp: Simpler yet better video prediction. In: CVPR

    2022

  287. [295]

    In: CVPR

    Garcia-Hernando G, Yuan S, Baek S, Kim TK (2018) First-person hand action benchmark with rgb-d videos and 3d hand pose annotations. In: CVPR

    2018

  288. [296]

    Gat I, Schwartz I, Schwing A (2021) Perceptual score: What data modalities does your model perceive? NeurIPS

    2021

  289. [297]

    In: WACV

    Ge R, Gao J, Chen K, Nevatia R (2019) Mac: Mining activity concepts for language-based temporal localization. In: WACV

    2019

  290. [298]

    In: ECCV

    Ge S, Hayes T, Yang H, Yin X, Pang G, Jacobs D, Huang JB, Parikh D (2022 a ) Long video generation with time-agnostic vqgan and time-sensitive transformer. In: ECCV

    2022

  291. [299]

    In: CVPR

    Ge Y, Ge Y, Liu X, Li D, Shan Y, Qie X, Luo P (2022 b ) Bridging video-text retrieval with multiple choice questions. In: CVPR

    2022

  292. [300]

    In: ICASSP

    Gemmeke JF, Ellis DP, Freedman D, Jansen A, Lawrence W, Moore RC, Plakal M, Ritter M (2017) Audio set: An ontology and human-labeled dataset for audio events. In: ICASSP

    2017

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.