Pith. sign in

REVIEW 5 major objections 6 minor 4 cited by

LLaVA-MR: Large Language-and-Vision Assistant for Video Moment Retrieval

T0 review · 5 major / 6 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 By sampling densely, flagging the most dynamic frames, and compressing the rest, LLaVA-MR outperforms prior state-of-the-art methods on QVHighlights, Charades-STA, and ActivityNet Captions.

desk verdict Plausible engineering recipe let down by an un-isolated key module and some sloppy evaluation; worth review, but fix the ablations first. read the letter →

arxiv 2411.14505 v1 pith:3PT6EIIR submitted 2024-11-21 cs.CV

Weiheng Lu , Jian Li , An Yu , Ming-Ching Chang , Shengpeng Ji , Min Xia This is my paper · ORCID
classification cs.CV
keywords videomomentretrievalmultimodallargelanguagemodeldenseframesamplingtimeencodinginformativeselectiondynamictokencompressionQVHighlightsCharades-STA
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

LLaVA-MR is a recipe for making a generative multimodal large language model do video moment retrieval on long videos, where sparse frame sampling and limited LLM context normally force a trade-off between coverage and precision. The paper claims that three coordinated changes—dense sampling with a per-frame time token, selection of the most dynamic frames, and compression of the remaining frame tokens—let the model locate query-relevant intervals better than the leading MLLM baseline and prior transformer-based methods. On QVHighlights test it reports 76.59 R1@0.5 and 69.41 mAP@0.5, on Charades-STA 70.65 R1@0.5, and on ActivityNet Captions 55.16 R1@0.5, each above Mr. BLIP. If the claim holds, it suggests the bottleneck for MLLM-based moment retrieval is not the language model's understanding but how frames are selected and tokenized, and that task-specific prediction heads are not needed.

What carries the argument

The load-bearing machinery is the interleaved multimodal token sequence fed to the LLM, built by three modules. DFTE increases the number of sampled frames (60 for Charades-STA, 80 for QVHighlights) and places a time token before every frame embedding, using relative frame indices when sampled frames per second is at least one and timestamps otherwise. IFS computes the L2 distance between adjacent frame feature maps, smooths the resulting signal with a Gaussian filter, and treats the top-k frames as key frames that retain their full Q-Former tokens. DTC compresses the remaining non-key frames, either by average pooling or by keeping only the half of Q-Former queries with the highest variance across frames, which the paper argues preserves dynamic content. The sequence, marked with begin and end tokens for time and frame segments, is fed to the LLM, which outputs a nested list of predicted start and end times.

What would settle it

Run a controlled ablation that keeps the total token budget identical but replaces Informative Frame Selection's top-distance key frames with the same number of uniformly random key frames, repeated over several seeds on Charades-STA and QVHighlights; if the R1@0.5 and mAP differences stay within noise, the adjacent-feature-distance criterion is not doing the claimed work.

Watch

Extended reading notes

Core claim

The central claim is that moment retrieval can be reframed as an open-ended text-generation problem solved by a pretrained MLLM, provided the video is represented as a dense but compressed interleaved sequence of time tokens and frame embeddings. Dense Frame and Time Encoding supplies fine-grained spatial-temporal features by sampling many frames and prefixing each with a time token chosen by the frame sampling rate. Informative Frame Selection marks the frames with the largest Gaussian-smoothed adjacent-feature L2 distances as key frames, capturing event boundaries, and Dynamic Token Compression reduces the token count of non-key frames by keeping only Q-Former queries with the highest variance across frames. The paper reports that this combination reaches state-of-the-art results on QVHighlights, Charades-STA, and ActivityNet Captions, with the largest gains on the longer-video dataset.

Load-bearing premise

The whole gain rests on the assumption that the frames whose adjacent visual features change most sharply are the frames that matter for locating the queried moment, and that the remaining frames can be compressed without losing information the model needs.

Editorial extensions

If this is right

  • MLLM-based moment retrieval can use far more frames than the LLM context would naively allow, because most frames are reduced to a few high-variance query tokens.
  • The choice of time representation matters: relative frame indices beat rounded timestamps when frames are denser than one per second, and timestamps beat indices when frames are sparser.
  • Variance-based token compression outperforms both no compression and average pooling, indicating that high-variance Q-Former queries carry the dynamic information the task needs.
  • The full pipeline runs faster than the sparse-sampling baseline Mr. BLIP at the same frame count, because compression more than offsets the cost of frame selection.
  • The method transfers to longer and more complex videos, with larger absolute gains on QVHighlights than on Charades-STA.

Reading between the lines

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

  • If the adjacent-feature-distance criterion is truly what selects key frames, the same selection module should transfer to other long-video MLLM tasks with event boundaries, such as temporal action localization or highlight detection, without retraining the selector.
  • A possible extension the paper does not develop is query-aware key-frame selection: IFS is query-agnostic, so reranking candidate key frames by relevance to the text could improve recall on moments that are visually static but semantically relevant.
  • The sampling-rate rule for time encoding suggests a continuous time embedding instead of integer indices or rounded timestamps, which should remove the token-collision problem and may improve both dense and sparse sampling regimes.
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

5 major / 6 minor

Summary. The paper proposes LLaVA-MR, an MLLM-based video moment retrieval method built on BLIP-2. It introduces three modules: Dense Frame and Time Encoding (DFTE), which samples more frames and chooses either relative frame indices or timestamps as time tokens depending on the sampling rate; Informative Frame Selection (IFS), which selects top-k frames by smoothed adjacent-feature L2 distance; and Dynamic Token Compression (DTC), which compresses non-key frame tokens either by average pooling or by retaining high-variance Q-Former queries. The authors report state-of-the-art results on QVHighlights test (76.59 R1@0.5, 69.41 mAP@0.5), Charades-STA (70.65 R1@0.5), and ActivityNet Captions (55.16 R1@0.5), with ablations on Charades-STA showing monotone gains. The implementation is promised to be open-sourced upon acceptance.

Significance. If the reported results are reproducible, LLaVA-MR is a simple and potentially useful recipe for adapting frozen-image MLLMs to moment retrieval: dense sampling plus a hand-crafted frame-selection and token-compression scheme. A genuine strength is that the headline QVHighlights test numbers come from the evaluation server, so those labels were not directly visible to the authors. The paper also reports a modest inference-time advantage over Mr. BLIP. However, the specific mechanism claimed to be most novel, IFS, is not isolated in the ablations, and the experimental protocol contains enough selection-on-test-set and post-processing ambiguity that the central claim is not yet established to the standard expected for this venue. The work is nonetheless a reasonable engineering contribution that could become acceptable after substantial revision.

major comments (5)
  1. [§4.3, Tables 2 and 5] The contribution of IFS is never isolated. Row (c) to (d) of Table 2 enables IFS and DTC together, so the +2.13 R1@0.5 gain belongs to the combination, not to IFS alone. Table 5 then varies only the compression method (none, average pooling, variance-based DTC) with IFS fixed, so the selection criterion itself is not tested. Without an ablation that compares IFS against random key-frame selection, and against uniform/dense sampling at the same token budget, the paper's central claim that adjacent-feature L2 distance identifies informative frames is not supported. The interpretation is further complicated because the sparse baseline in Table 2 (a) scores 65.48 R1@0.5, well below Mr. BLIP's 69.31 on the same Charades-STA test set, so the module gains are measured from a weaker starting point than the leading baseline.
  2. [§4.3, Fig. 6 and Table 8] For Charades-STA, the hyperparameters N=60 and k=32 are selected by optimizing performance on the Charades-STA test set, not on a held-out validation split. This is test-set selection and can inflate the reported gains. The QVHighlights hyperparameters are selected on the validation set (Table 8), but Charades-STA, where the full ablation and final SOTA comparison are run, is not handled this way. Please report a validation-based selection procedure for both datasets, or clearly state how many configurations were tried and account for the selection in the reported numbers.
  3. [§4.1 and Supplementary §7] The final reported numbers depend on post-processing heuristics described only qualitatively. The algorithm swaps start and end times, extracts number pairs from malformed outputs, and substitutes [-1,-1] for invalid predictions; such heuristics can change recall and mAP. The paper does not quantify the effect of post-processing on the final scores, nor does it state whether the Mr. BLIP baseline numbers were obtained with equivalent post-processing. This is necessary for a fair SOTA comparison, especially because the paper states that the heuristics are applied 'to improve prediction accuracy.'
  4. [§4.3, Tables 1 and 3] The ablation tables are internally inconsistent. Table 1 reports LLaVA-MR at 78.13 R1@0.5 on QVHighlights validation, while Table 3 reports at most 76.06 for either time representation; Table 2 row (c) gives 68.52 R1@0.5 on Charades-STA, while Table 3 gives 67.83 or 66.37. The text also states that 'using frame indices as time tokens led to better performance' on QVHighlights, but Table 3 shows timestamps are better by 1.29 points. The configuration underlying each table (which modules, special tokens, and post-processing are active) must be stated, and the contradiction resolved, before the time-encoding design claim can be evaluated.
  5. [§4.1 and §4.2] All experiments appear to be single runs with no error bars. Given that the headline improvements over Mr. BLIP are on the order of 0.5 to 2 points, and the ablations involve choices of N, k, and T, reporting mean and standard deviation over at least three seeds, or providing per-seed numbers, is necessary to establish that the differences are not optimization noise. This is particularly important because the Charades-STA configuration was selected on the test set.
minor comments (6)
  1. [Abstract and Conclusion] The abstract says the method outperforms 11 state-of-the-art methods, while the conclusion says ten leading methods; please reconcile the count.
  2. [§4.3, Fig. 6] The text says 'As illustrated in Figure 4' when discussing the effect of k on performance, but the relevant figure appears to be Figure 6 (hyperparameter optimization results).
  3. [§3.3, Eq. (2)] Equation (2) sets \hat d_0 = max(d), which forces the first frame to be a key frame even though there is no preceding frame; this choice is not motivated and deserves a one-sentence justification.
  4. [§4.1] The LoRA rank and alpha are not reported; including them would improve reproducibility.
  5. [Table 1] For Charades-STA, mAP@0.5 and mAP@0.75 are reported only for LLaVA-MR and not for Mr. BLIP; adding these numbers for the closest baseline would help the reader assess the comparison.
  6. [§3.4 and Table 7] Variance-Based DTC is described as retaining the top half of queries by variance, but Table 7 later tunes T (the compressed token count) as a hyperparameter; please clarify whether the 'top half' rule is a fixed design choice or a tuned hyperparameter.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the headline results are grounded in external benchmark comparisons, and the module designs are heuristic preprocessing steps whose value is tested downstream rather than derived from the target metric by construction.

full rationale

I walked the claimed derivation chain. DFTE, IFS, and DTC are defined by their own equations (Eq. 2 and Eq. 3 for IFS, the variance criterion in Sec. 3.4 for DTC, and the sampling-rate rule in Sec. 3.2 for DFTE). None of these definitions involves the moment-retrieval metric or the annotated moments the model is asked to predict; their contribution is assessed by downstream R1/mAP numbers on public benchmarks, which are independent of the paper's own construction. In particular, the QVHighlights test results are obtained through the evaluation server with hidden labels, so the claimed improvements over Mr. BLIP cannot reduce to the authors' definitions or fitted values. The only self-citations appear in related-work surveys and method background, and they are not load-bearing for the central claim. The legitimate concerns in this paper are about experimental attribution and overfitting, not circularity: IFS is never isolated in Table 2 (row (c) to (d) enables IFS and DTC simultaneously), hyperparameters N, k, T and the time-encoding choice are tuned on the Charades-STA test set and QVHighlights validation set, and Table 5's no-compression row has the same score as Table 2(c), suggesting IFS alone may add little. However, these are confounded-ablation and test-set-selection issues, not cases where an output is equivalent to its input by construction or where a fitted parameter is renamed as a prediction. No equation is defined in terms of the result it is claimed to predict, so the derivation chain is not circular.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

The central claim rests on standard pretrained components (BLIP-2, Q-Former) taken from prior work, plus four fitted hyperparameters and two domain assumptions about what makes frames informative. No new physical entities are introduced.

free parameters (4)
  • N (number of sampled frames) = 60 for Charades-STA, 80 for QVHighlights
    Chosen by hyperparameter search on Charades-STA test and QVHighlights validation (Fig. 6, Table 8); performance varies with N.
  • k (number of key frames in IFS) = 32 for both datasets
    Tuned in Section 4.3 and Section 6.2; extremes degrade performance, so this is a fitted value.
  • T (post-compression token count per frame after DTC) = 16
    Table 7 shows best at 1:2 ratio (16 of 32 tokens); tuned on Charades-STA test set.
  • Time encoding choice (relative frame indices vs timestamps) = relative indices for Charades-STA; timestamps for QVHighlights
    Selected per dataset based on the Rframes heuristic and ablations (Table 3); the prose contradicts the QVHighlights table.
assumptions (3)
  • domain assumption Pretrained BLIP-2 provides transferable visual and language representations for video moment retrieval.
    The model is built on frozen image encoder and Q-Former from BLIP-2; no evidence beyond prior work that these features suffice.
  • domain assumption Frames with high adjacent feature distance tend to mark event boundaries and are more informative for retrieval.
    IFS design hypothesis, stated in Section 3.3 and illustrated with one video in Fig. 5; no quantitative verification.
  • domain assumption Variance of Q-Former queries across frames correlates with informativeness for moment retrieval.
    Variance-Based DTC selection rule, Section 3.4; heuristic, only tested indirectly through Table 5.

how reviews work

0 comments
Cite this review

Pith. "Pith review of LLaVA-MR: Large Language-and-Vision Assistant for Video Moment Retrieval." pith.science (2026). https://pith.science/paper/3PT6EIIR

@misc{pith2026241114505,
  author       = {Pith},
  title        = {Pith review of: LLaVA-MR: Large Language-and-Vision Assistant for Video Moment Retrieval},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3PT6EIIR}},
  note         = {Machine review of arXiv:2411.14505}
}
read the original abstract

Multimodal Large Language Models (MLLMs) are widely used for visual perception, understanding, and reasoning. However, long video processing and precise moment retrieval remain challenging due to LLMs' limited context size and coarse frame extraction. We propose the Large Language-and-Vision Assistant for Moment Retrieval (LLaVA-MR), which enables accurate moment retrieval and contextual grounding in videos using MLLMs. LLaVA-MR combines Dense Frame and Time Encoding (DFTE) for spatial-temporal feature extraction, Informative Frame Selection (IFS) for capturing brief visual and motion patterns, and Dynamic Token Compression (DTC) to manage LLM context limitations. Evaluations on benchmarks like Charades-STA and QVHighlights demonstrate that LLaVA-MR outperforms 11 state-of-the-art methods, achieving an improvement of 1.82% in R1@0.5 and 1.29% in mAP@0.5 on the QVHighlights dataset. Our implementation will be open-sourced upon acceptance.

Figures

Figures reproduced from arXiv: 2411.14505 by the authors.

Figure 1
Figure 1. Advantages and Comparison of LLaVA-MR over prior [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. LLaVA-MR model overview. We leverage a pretrained MLLM such as BLIP-2[ [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Two compression methods in DTC. (a) Average Pooling reduces tokens by averaging feature values within a window of 2, where f q i ∈ N q . (b) Variance-Based DTC calculates the variance of each query in the Q-Former across all frames (where Q is the number of queries), sorts queries by variance, and selects the top half of queries to retain tokens focusing on dynamic content. ˆdi = ( max(d), if i = 0 G(∥∆f v i ∥2), if… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Qualitative results on the Charades-STA and QVHighlights datasets, with ground truth segments for query events alongside [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 6
Figure 6. Figure 6: Hyperparameter optimization results on the Charades [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 5
Figure 5. Figure 5: Temporal correspondence between video frames and ad [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 7
Figure 7. Figure 7: Qualitative results across various types of videos, with ground truth segments for query events alongside highlighted predicted [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

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

  1. DisTime: Distribution-based Time Representation for Video Large Language Models

    cs.CV 2025-05 conditional novelty 7.0 of 10

    A single learnable time token, decoded into a probability distribution over time bins, improves temporal grounding in Video-LLMs and is trained partly on a new 1.25M-event pseudo-labeled dataset.

  2. Sparse-Dense Side-Tuner for efficient Video Temporal Grounding

    cs.CV 2025-07 conditional novelty 6.0 of 10

    SDST is a parameter-efficient, anchor-free side-tuning architecture for video temporal grounding that matches or beats state-of-the-art methods with about 73% fewer trainable parameters.

  3. SMART: Shot-Aware Multimodal Video Moment Retrieval with Audio-Enhanced MLLM

    cs.CV 2025-11 conditional novelty 5.0 of 10

    SMART, an audio-enhanced MLLM with shot-aware token compression, reports new state-of-the-art moment retrieval accuracy on Charades-STA and QVHighlights.

  4. A Survey on Video Temporal Grounding with Multimodal Large Language Model

    cs.CV 2025-08 unverdicted novelty 3.0 of 10

    A taxonomized review of video temporal grounding with multimodal large language models, covering model roles, training paradigms, feature processing, benchmarks, and open problems.

Reference graph

Works this paper leans on

55 extracted references · 42 canonical work pages · cited by 4 Pith papers

  1. [1]

    Flamingo: a visual language model for few-shot learning

    Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al. Flamingo: a visual language model for few-shot learning. In NeurIPS, pages 23716–23736, 2022. 2

    2022

  2. [2]

    Localizing mo- ments in video with natural language

    Lisa Anne Hendricks, Oliver Wang, Eli Shechtman, Josef Sivic, Trevor Darrell, and Bryan Russell. Localizing mo- ments in video with natural language. In ICCV, pages 5803– 5812, 2017. 2

    work page 2017

  3. [3]

    Openflamingo: An open-source frame- work for training large autoregressive vision-language mod- els, 2023

    Anas Awadalla, Irena Gao, Josh Gardner, Jack Hessel, Yusuf Hanafy, Wanrong Zhu, Kalyani Marathe, Yonatan Bitton, Samir Gadre, Shiori Sagawa, Jenia Jitsev, Simon Kornblith, Pang Wei Koh, Gabriel Ilharco, Mitchell Wortsman, and Ludwig Schmidt. Openflamingo: An open-source frame- work for training large autoregressive vision-language mod- els, 2023. 2

    work page 2023

  4. [4]

    CTRN: Class-Temporal Relational Network for Action Detection

    Rui Dai, Srijan Das, and Francois Bremond. Ctrn: Class- temporal relational network for action detection. arXiv preprint arXiv:2110.13473, 2021. 2

    work page Pith review arXiv 2021

  5. [5]

    Ms-tct: Multi-scale temporal con- vtransformer for action detection

    Rui Dai, Srijan Das, Kumara Kahatapitiya, Michael S Ryoo, and Franc ¸ois Br´emond. Ms-tct: Multi-scale temporal con- vtransformer for action detection. In CVPR, pages 20041– 20051, 2022. 2

    work page 2022

  6. [6]

    Instructblip: Towards general- purpose vision-language models with instruction tuning,

    Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, and Steven Hoi. Instructblip: Towards general- purpose vision-language models with instruction tuning,

  7. [7]

    Tall: Temporal activity localization via language query

    Jiyang Gao, Chen Sun, Zhenheng Yang, and Ram Nevatia. Tall: Temporal activity localization via language query. In ICCV, pages 5267–5275, 2017. 2, 5, 1

    work page 2017

  8. [8]

    Video action transformer network

    Rohit Girdhar, Joao Carreira, Carl Doersch, and Andrew Zis- serman. Video action transformer network. In CVPR, pages 244–253, 2019. 2

    work page 2019

Show all 55 references
  1. [9]

    Lora: Low-rank adaptation of large language models

    Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021. 5

    2021 arXiv

  2. [10]

    Knowing where to focus: Event-aware transformer for video grounding, 2023

    Jinhyun Jang, Jungin Park, Jin Kim, Hyeongjun Kwon, and Kwanghoon Sohn. Knowing where to focus: Event-aware transformer for video grounding, 2023. 6

    2023

  3. [11]

    Efficient multimodal large language models: A survey

    Yizhang Jin, Jian Li, Yexin Liu, Tianjun Gu, Kai Wu, Zhengkai Jiang, Muyang He, Bo Zhao, Xin Tan, Zhenye Gan, et al. Efficient multimodal large language models: A survey. arXiv preprint arXiv:2405.10739, 2024. 1

    2024

  4. [12]

    Dense-captioning events in videos,

    Ranjay Krishna, Kenji Hata, Frederic Ren, Li Fei-Fei, and Juan Carlos Niebles. Dense-captioning events in videos,

  5. [13]

    Temporal convolutional networks for ac- tion segmentation and detection

    Colin Lea, Michael D Flynn, Rene Vidal, Austin Reiter, and Gregory D Hager. Temporal convolutional networks for ac- tion segmentation and detection. In CVPR, pages 156–165,

  6. [14]

    Berg, and Mohit Bansal

    Jie Lei, Tamara L. Berg, and Mohit Bansal. Qvhighlights: Detecting moments and highlights in videos via natural lan- guage queries, 2021. 1, 6

    2021

  7. [15]

    Detecting mo- ments and highlights in videos via natural language queries

    Jie Lei, Tamara L Berg, and Mohit Bansal. Detecting mo- ments and highlights in videos via natural language queries. NeurIPS, 34:11846–11858, 2021. 2, 5

    2021

  8. [16]

    Mimic-it: Multi-modal in-context instruction tuning, 2023

    Bo Li, Yuanhan Zhang, Liangyu Chen, Jinghao Wang, Fanyi Pu, Jingkang Yang, Chunyuan Li, and Ziwei Liu. Mimic-it: Multi-modal in-context instruction tuning, 2023. 2

    2023

  9. [17]

    Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models, 2023

    Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models, 2023. 3

    2023

  10. [18]

    A survey on benchmarks of multimodal large language models,

    Jian Li, Weiheng Lu, Hao Fei, Meng Luo, Ming Dai, Min Xia, Yizhang Jin, Zhenye Gan, Ding Qi, Chaoyou Fu, Ying Tai, Wankou Yang, Yabiao Wang, and Chengjie Wang. A survey on benchmarks of multimodal large language models,

  11. [19]

    Videochat: Chat-centric video understanding, 2024

    KunChang Li, Yinan He, Yi Wang, Yizhuo Li, Wenhai Wang, Ping Luo, Yali Wang, Limin Wang, and Yu Qiao. Videochat: Chat-centric video understanding, 2024. 2

    2024

  12. [20]

    Fast learning of temporal action proposal via dense boundary generator

    Chuming Lin, Jian Li, Yabiao Wang, Ying Tai, Donghao Luo, Zhipeng Cui, Chengjie Wang, Jilin Li, Feiyue Huang, and Rongrong Ji. Fast learning of temporal action proposal via dense boundary generator. In Proceedings of the AAAI conference on artificial intelligence , pages 11499–11506,

  13. [21]

    Univtg: Towards unified video- language temporal grounding, 2023

    Kevin Qinghong Lin, Pengchuan Zhang, Joya Chen, Shra- man Pramanick, Difei Gao, Alex Jinpeng Wang, Rui Yan, and Mike Zheng Shou. Univtg: Towards unified video- language temporal grounding, 2023. 6

    2023

  14. [22]

    Bsn: Boundary sensitive network for temporal action proposal generation

    Tianwei Lin, Xu Zhao, Haisheng Su, Chongjing Wang, and Ming Yang. Bsn: Boundary sensitive network for temporal action proposal generation. In ECCV, pages 3–19, 2018. 2

    2018

  15. [23]

    Visual instruction tuning, 2023

    Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning, 2023. 2

    2023

  16. [24]

    Umt: Unified multi-modal transformers for joint video moment retrieval and highlight detection

    Ye Liu, Siyuan Li, Yang Wu, Chang-Wen Chen, Ying Shan, and Xiaohu Qie. Umt: Unified multi-modal transformers for joint video moment retrieval and highlight detection. In CVPR, pages 3042–3051, 2022. 2

    2022

  17. [25]

    Decoupled weight decay regularization, 2019

    Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization, 2019. 5

    2019

  18. [26]

    Valley: Video assistant with large language model enhanced ability, 2023

    Ruipu Luo, Ziwang Zhao, Min Yang, Junwei Dong, Da Li, Pengcheng Lu, Tao Wang, Linmei Hu, Minghui Qiu, and Zhongyu Wei. Valley: Video assistant with large language model enhanced ability, 2023. 2

    2023

  19. [27]

    Video-chatgpt: Towards detailed video understanding via large vision and language models, 2024

    Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fa- had Shahbaz Khan. Video-chatgpt: Towards detailed video understanding via large vision and language models, 2024. 2

    2024

  20. [28]

    The surprising effectiveness of multimodal large language models for video moment retrieval, 2024

    Boris Meinardus, Anil Batra, Anna Rohrbach, and Marcus Rohrbach. The surprising effectiveness of multimodal large language models for video moment retrieval, 2024. 1, 2, 3, 5, 6, 7

    2024

  21. [29]

    Query-dependent video representa- tion for moment retrieval and highlight detection, 2023

    WonJun Moon, Sangeek Hyun, SangUk Park, Dongchan Park, and Jae-Pil Heo. Query-dependent video representa- tion for moment retrieval and highlight detection, 2023. 6

    2023

  22. [30]

    Correlation-guided query-dependency calibration for video temporal grounding, 2024

    WonJun Moon, Sangeek Hyun, SuBeen Lee, and Jae-Pil Heo. Correlation-guided query-dependency calibration for video temporal grounding, 2024. 1, 6

    2024

  23. [31]

    Local- global video-text interactions for temporal grounding

    Jonghwan Mun, Minsu Cho, and Bohyung Han. Local- global video-text interactions for temporal grounding. In CVPR, pages 10810–10819, 2020. 2

    2020

  24. [32]

    Pat: Position-aware transformer for dense multi-label action detection

    Faegheh Sardari, Armin Mustafa, Philip JB Jackson, and Adrian Hilton. Pat: Position-aware transformer for dense multi-label action detection. In CVPR, pages 2988–2997,

  25. [33]

    Temporal action localization in untrimmed videos via multi-stage cnns

    Zheng Shou, Dongang Wang, and Shih-Fu Chang. Temporal action localization in untrimmed videos via multi-stage cnns. In CVPR, pages 1049–1058, 2016. 2

    2016

  26. [34]

    Vlg-net: Video-language graph matching network for video grounding, 2021

    Mattia Soldan, Mengmeng Xu, Sisi Qu, Jesper Tegner, and Bernard Ghanem. Vlg-net: Video-language graph matching network for video grounding, 2021. 1

    2021

  27. [35]

    Learning grounded vision-language representation for versatile understanding in untrimmed videos, 2023

    Teng Wang, Jinrui Zhang, Feng Zheng, Wenhao Jiang, Ran Cheng, and Ping Luo. Learning grounded vision-language representation for versatile understanding in untrimmed videos, 2023. 1

    2023

  28. [36]

    Internvideo2: Scaling foundation models for multimodal video understanding, 2024

    Yi Wang, Kunchang Li, Xinhao Li, Jiashuo Yu, Yinan He, Chenting Wang, Guo Chen, Baoqi Pei, Ziang Yan, Rongkun Zheng, Jilan Xu, Zun Wang, Yansong Shi, Tianxiang Jiang, Songze Li, Hongjie Zhang, Yifei Huang, Yu Qiao, Yali Wang, and Limin Wang. Internvideo2: Scaling foundation mo...

    2024

  29. [37]

    Unloc: A unified framework for video localization tasks, 2023

    Shen Yan, Xuehan Xiong, Arsha Nagrani, Anurag Arnab, Zhonghao Wang, Weina Ge, David Ross, and Cordelia Schmid. Unloc: A unified framework for video localization tasks, 2023. 6, 1

    2023

  30. [38]

    Unloc: A unified framework for video localization tasks, 2023

    Shen Yan, Xuehan Xiong, Arsha Nagrani, Anurag Arnab, Zhonghao Wang, Weina Ge, David Ross, and Cordelia Schmid. Unloc: A unified framework for video localization tasks, 2023. 1, 6

    2023

  31. [39]

    Self-chained image-language model for video localization and question answering, 2023

    Shoubin Yu, Jaemin Cho, Prateek Yadav, and Mohit Bansal. Self-chained image-language model for video localization and question answering, 2023. 6

    2023

  32. [40]

    Semantic conditioned dynamic modulation for tempo- ral sentence grounding in videos

    Yitian Yuan, Lin Ma, Jingwen Wang, Wei Liu, and Wenwu Zhu. Semantic conditioned dynamic modulation for tempo- ral sentence grounding in videos. NeurIPS, 32, 2019. 2

    2019

  33. [41]

    Graph con- volutional networks for temporal action localization

    Runhao Zeng, Wenbing Huang, Mingkui Tan, Yu Rong, Peilin Zhao, Junzhou Huang, and Chuang Gan. Graph con- volutional networks for temporal action localization. In ICCV, pages 7094–7103, 2019. 2

    2019

  34. [42]

    Dense regression network for video grounding, 2020

    Runhao Zeng, Haoming Xu, Wenbing Huang, Peihao Chen, Mingkui Tan, and Chuang Gan. Dense regression network for video grounding, 2020. 1

    2020

  35. [43]

    Unimd: Towards unifying moment retrieval and temporal ac- tion detection, 2024

    Yingsen Zeng, Yujie Zhong, Chengjian Feng, and Lin Ma. Unimd: Towards unifying moment retrieval and temporal ac- tion detection, 2024. 1, 6

    2024

  36. [44]

    Actionformer: Localizing moments of actions with transformers

    Chen-Lin Zhang, Jianxin Wu, and Yin Li. Actionformer: Localizing moments of actions with transformers. In ECCV, pages 492–510. Springer, 2022. 2

    2022

  37. [45]

    Man: Moment alignment network for natural language moment retrieval via iterative graph adjustment

    Da Zhang, Xiyang Dai, Xin Wang, Yuan-Fang Wang, and Larry S Davis. Man: Moment alignment network for natural language moment retrieval via iterative graph adjustment. In CVPR, pages 1247–1257, 2019. 2

    2019

  38. [46]

    Video-llama: An instruction-tuned audio-visual language model for video un- derstanding, 2023

    Hang Zhang, Xin Li, and Lidong Bing. Video-llama: An instruction-tuned audio-visual language model for video un- derstanding, 2023. 2

    2023

  39. [47]

    Llama-adapter: Efficient fine-tuning of language models with zero-init attention, 2024

    Renrui Zhang, Jiaming Han, Chris Liu, Peng Gao, Aojun Zhou, Xiangfei Hu, Shilin Yan, Pan Lu, Hongsheng Li, and Yu Qiao. Llama-adapter: Efficient fine-tuning of language models with zero-init attention, 2024. 2

    2024

  40. [48]

    Learning 2d temporal adjacent networks for moment local- ization with natural language

    Songyang Zhang, Houwen Peng, Jianlong Fu, and Jiebo Luo. Learning 2d temporal adjacent networks for moment local- ization with natural language. In AAAI, pages 12870–12877,

  41. [49]

    Temporal action detection with structured segment networks

    Yue Zhao, Yuanjun Xiong, Limin Wang, Zhirong Wu, Xi- aoou Tang, and Dahua Lin. Temporal action detection with structured segment networks. In ICCV, pages 2914–2923,

  42. [50]

    Minigpt-4: Enhancing vision-language understanding with advanced large language models, 2023

    Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mo- hamed Elhoseiny. Minigpt-4: Enhancing vision-language understanding with advanced large language models, 2023. 2

    2023

  43. [51]

    Enriching local and global contexts for temporal action localization

    Zixin Zhu, Wei Tang, Le Wang, Nanning Zheng, and Gang Hua. Enriching local and global contexts for temporal action localization. In ICCV, pages 13516–13525, 2021. 2 LLaV A-MR: Large Language-and-Vision Assistant for Video Moment Retrieval Supplementary Material

    2021

  44. [52]

    Additional Experiments We present additional experimental results, including a comparison with state-of-the-art methods on the Activi- tyNet Captions dataset and more findings from hyperparam- eter optimization. 6.1. Comparison on ActivityNet Captions Dataset ActivityNet Capti...

  45. [53]

    [[-1, -1]]

    Post-Processing of LLM Outputs Despite training LLMs specifically for the moment re- trieval task and instructing them to output results in the for- mat of a nested list of moments: y = [[tstart 1 , tend 1 ], [tstart 2 , tend 2 ], . . .] LLMs can still produce outputs that dev...

  46. [54]

    automated devices operating in a modern factory

    More Qualitative Results One of the key advantages of using MLLMs for moment retrieval tasks is their strong generalization and adaptabil- ity. Unlike transformer models with task-specific prediction heads, which are prone to annotation bias, MLLMs utilize cross-modal represen...

  47. [55]

    This section explores po- tential future directions for enhancing the performance of MLLMs in moment retrieval tasks

    Future research potential The moment retrieval task has a wide range of applica- tions, including video summarization, content-based re- trieval, multimedia search, video recommendation systems, and educational video indexing. This section explores po- tential future direction...

Pith tools

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