Pith. sign in

REVIEW 3 major objections 5 minor 100 references

ATCTrack: Aligning Target-Context Cues with Dynamic Target States for Robust Vision-Language Tracking

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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

Pith's one-line read ATCTrack is a vision-language tracker that aligns both visual and textual target-context cues with the target's changing state, and it reports state-of-the-art precision on MGIT, TNL2K, and LaSOText.

desk verdict Solid VLT paper with a genuinely new text-only target-word classifier; the 96.7% accuracy figure is inflated by an oracle-k evaluation, but the main tracking gains survive. read the letter →

arxiv 2507.19875 v1 pith:ZF5K747C submitted 2025-07-26 cs.CV

X. Feng , S. Hu , X. Li , D. Zhang , M. Wu , J. Zhang , X. Chen , K. Huang This is my paper · ORCID
classification cs.CV
keywords vision-languagetrackingtarget-contextmodelingdynamictargetstatewordclassificationlong-termvisualmemorymultimodalguidancebenchmarkevaluation
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

ATCTrack sets out to show that vision-language trackers fail in long-term videos because the template patch and language description given in the first frame only match the target's initial state, and that this can be fixed by aligning target-context cues with the target's current state. It builds a memory of updated visual target-context features and, on the text side, learns to identify which words name the target and which merely describe context, then recalibrates the context words using the latest visual memory. The full system reports state-of-the-art scores on MGIT, TNL2K, and LaSOText, with ATCTrack-B raising precision by 6.4%, 4.3%, and 3.5% over previous best results. A sympathetic reading is that dynamic cue alignment is a general remedy for prompt staleness, and that text-only target-word awareness is sufficient for using language cues well.

What carries the argument

The central machinery is the pair of target-context guidance modules plus the memory storage module (MSM). The visual path constructs a target-context distribution map $h^t$ from feature similarity between template and search, uses it to weight search tokens, and builds a memory unit $m^t$ for each timestep via cross-attention over the [CLS] token and the stored sliding-window memory. The textual path identifies target words with $\mathrm{MLP}(f_L)$, a multi-label binary classifier over text tokens, and then recalibrates the initial text features through attention conditioned on the enhanced target feature formed from target words and visual memory. The updated search features are passed to a CNN prediction head, with an overall loss combining classification, IoU, L1, and a binary cross-entropy term for target-word labels. These components carry the argument by showing that each explicit mechanism contributes to the reported benchmark gains.

What would settle it

Take a held-out set of sentences from MGIT and TNL2K, have human annotators mark target words, then run ATCTrack's trained MLP on those sentences and compute target-word accuracy on that split; if accuracy falls to the level of the similarity-based method (29.9%) or the tracker's benchmark gains disappear when the classifier is retrained on a clean held-out label set, the central claim would be refuted.

Watch

Extended reading notes

Core claim

The paper's central claim is that target and context cues should track the target's changing state rather than remain frozen to the first-frame prompt. For vision, ATCTrack computes a target-context distribution heatmap by comparing search and template features, then folds that heatmap into a temporal memory of [CLS] features, so each frame's memory unit carries current and historical target-context information. For text, it reframes word interpretation as text-only classification: a lightweight MLP assigns each token a target-word probability, trained with labels produced automatically by an LLM annotation pipeline, and the identified target words are used to calibrate context words against the latest visual memory. In the paper's reported comparisons, this dual alignment gives ATCTrack-B a 73.7 AUC on MGIT's action split and precision gains over prior state-of-the-art results on TNL2K and LaSOText, with ablations attributing the gains to both guidance modules and to the target-word supervision.

Load-bearing premise

The method's gains rest on the assumption that the automatically generated target-word labels are correct on sentences the tracker has not seen during training; the paper checks only 50 sampled sentences and does not say whether the reported 96.7% accuracy comes from a held-out set.

Editorial extensions

If this is right

  • A tracker can keep using the original language description across long sequences: context words are recalibrated frame by frame against visual memory, so stale wording does not have to be regenerated.
  • Target-word awareness can be treated as a text-only classification problem, bypassing the fine-grained vision-text word alignment that earlier trackers rely on for grounding.
  • Representing target-context information as a global spatial heatmap stored in memory gives denser temporal cues than cropping the predicted bounding box, according to the paper's ablations.
  • Combining the textual and visual guidance modules yields complementary gains, so methods that address only one modality are leaving tracking accuracy on the table.

Reading between the lines

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

  • Not in the paper, but the text-only target-word classifier could transfer to other tasks where the prompt is fixed, such as referring-expression comprehension or visual grounding, because it removes the need for per-frame multimodal word alignment.
  • Not in the paper, but because the 96.7% target-word accuracy was checked on only 50 sampled sentences, a natural next check is to measure held-out accuracy on a larger stratified sample from MGIT and TNL2K; the stated comparison could otherwise be optimistic.
  • Not in the paper, but if dynamic cue alignment is the source of the gains, then a tracker that periodically refreshes the language description itself, rephrasing target attributes as they change, should show similar or larger improvements; that variant is not tested here.
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

3 major / 5 minor

Summary. The paper proposes ATCTrack, a vision-language tracker that dynamically aligns multimodal target-context cues with the evolving target state. For the visual modality, it constructs a target-context distribution map from template-search similarity and stores the resulting features in a sliding-window memory. For the textual modality, it trains a lightweight MLP (Eq. 1) on LLM-generated labels to identify target words, then uses a context-words calibration mechanism (Eqs. 2-4) that modulates initial text features with visual memory. The tracker is evaluated on MGIT, TNL2K, LaSOT, and LaSOText, with ablations in Tables 2-5. The paper claims new state-of-the-art results, including a 6.4% precision improvement on MGIT over the previous best, and reports 96.7% target-word classification accuracy for the textual module versus 29.9% for a vision-text similarity baseline.

Significance. The proposed architecture is well motivated: long-term tracking requires target and context cues to follow the target's changing state, and the paper offers concrete mechanisms for both modalities. If the benchmark results hold, the method provides a practical way to keep language and visual references fresh during long-term tracking. The strengths of the paper are its consistent benchmark tables, the fact that each proposed module contributes in the ablations (Tables 2-5), the use of external benchmark test splits for tracking evaluation, and the promised code/model release. The main weakness is the reliability of the target-word classification accuracy in Fig. 2, which is load-bearing for the textual-module motivation but is evaluated with an oracle-k procedure and without a stated held-out split; the headline LaSOText precision improvement in the abstract is also inconsistent with Table 1.

major comments (3)
  1. [Fig. 2(a), Appendix B.2, Sec. 3.2] The reported target-word classification accuracies (96.7% Acctarget and 98.9% Accall for ATCTrack; 29.9% for the baseline) are not established as stated. Appendix B.2 obtains the prediction p by selecting the top-k elements of per-word relevance scores, where k is the ground-truth number of target words. This oracle-k procedure uses the label being predicted and does not match the deployment of Eq. (1), where p_T is used as a soft weight in the textual guidance module rather than as a binarized top-k selection. In addition, the paper never states whether the MLP's accuracy is measured on a split held out from the 150-epoch training described in Sec. 4.1; if the accuracy is computed on training sentences, it is inflated by memorization. The authors should report classification accuracy with a fixed threshold on a clearly specified held-out set, or explicitly relabel the figure as a ranking metric under oracle-k and discuss the implications for the claimed advantage over vision-text similarity methods.
  2. [Abstract, Introduction, Table 1] The abstract and Introduction state that ATCTrack-B improves over the existing best results by 6.4%, 4.3%, and 3.5% in precision on MGIT, TNL2K, and LaSOText, respectively. Table 1 supports the MGIT figure (70.1 vs. 63.7 for MemVLT) and the TNL2K figure (73.6 vs. 69.3 for SUTrack-B384), but the LaSOText figure is not supported: the best existing precision in Table 1 is 61.7 (SUTrack-L224), or 60.5 among basic variants, while ATCTrack-B achieves 62.8, an improvement of at most 2.3 points. The claimed numbers should be corrected, or the comparison baseline used for each number should be explicitly defined.
  3. [Appendix B.1, Fig. 2(a), Introduction] The 29.9% baseline accuracy attributed to 'vision-text similarity-based methods' is measured with JointNLT, which the paper itself describes as a proxy because QueryNLT, TTCTrack, and OSDT have not released code. JointNLT is not one of the methods critiqued in the Introduction for failing to separate target and context words, and it is not trained for target-word classification. The comparison therefore does not substantiate the broad claim that existing VLTs 'struggle to discern which words pertain to the target or the context.' The authors should evaluate the actual methods where possible, or clearly present the JointNLT result as an approximate proxy and temper the claim accordingly.
minor comments (5)
  1. [Sec. 4.3, visual target-context modeling paragraph] The row references for Table 4 are inconsistent: the text says 'Tab. 4 (#3) adopts RoI processing' and 'Tab. 4 (#4) replaces h_t with the local mask,' but in Table 4 row #2 is 'w RoI' and row #3 is 'w search + crop mask.' Please align the text with the table.
  2. [Appendix A] The annotation quality check is based on only 50 randomly sampled sentences. Reporting the human-verification sample size and agreement rate, or conducting a larger verification, would make the LLM-label reliability claim more convincing given that these labels supervise the MLP in Eq. (1).
  3. [Sec. 4.1 and Appendix D] Several hyperparameters (memory length Lm=4, dynamic template update threshold 0.8 and interval 25) are fixed without ablation or justification. A brief sensitivity study or a statement that these follow prior work would strengthen the reproducibility of the results.
  4. [Fig. 2(a)] The accuracy values in Fig. 2(a) are presented as point estimates with no sentence count or variability measure; adding the number of test sentences and any error bars would help the reader interpret the 96.7% and 29.9% figures.
  5. [Table 1] Multiple rows report missing P Norm values for TNL2K (e.g., SUTrack-B224 and SUTrack-B384), which makes the normalized-precision comparison across methods incomplete; this is a reporting issue rather than a correctness issue.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: ATCTrack's tracking results are measured on external benchmark test splits, and its textual module is trained on LLM-generated labels rather than on the quantities it is claimed to predict.

full rationale

The paper's derivation chain is not circular. The central SOTA claim (Table 1) is supported by tracking AUC and precision numbers on public test splits (MGIT, TNL2K, LaSOT, LaSOText) with ground-truth boxes; these numbers are not outputs of the model's own fitted parameters. The textual target-word MLP (Eq. 1) is supervised with GPT-4o-generated target-word labels (Appendix A), which is an external-label distillation setup rather than a self-defined objective; the MLP does not define the labels it is evaluated against. The context-words calibration (Eqs. 2-4) and visual memory representation (Eqs. 5-8) are feed-forward attention mechanisms over text features, template features, and stored memory; they do not encode the benchmark metrics or the final tracking scores. Self-citations to MemVLT [25], CTVLT [24], and MGIT [33] appear as baselines and benchmark references, but the comparison numbers are external published results, so no load-bearing claim reduces to a self-citation. The paper's main validity caveats (Fig. 2's 96.7% target-word accuracy without an explicit held-out split, and Appendix B.2's use of ground-truth target-word count k to form top-k predictions) are evaluation-validity concerns, not cases where an equation reduces to its own input; they do not make the derivation circular.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The tracking results are produced by training a one-stream transformer on a composite of labeled and pseudo-labeled datasets; no new physical entity or conserved quantity is introduced. The load-bearing assumptions are empirical: the LLM-generated target-word labels are reliable, the template-search similarity heatmap is a faithful targetness measure, and the hand-chosen hyperparameters (memory length, loss weights, update threshold) are reasonable. None of these assumptions is tested with a dedicated experiment, which is where the main uncertainty lies.

free parameters (6)
  • Memory length (Lm) = 4
    Set in Sec. 4.1; controls how many historical heatmap-informed memory units are used in the cross-attention of Eq. (6). No ablation is reported for this value.
  • Loss weights = Lcls=1, Liou=2, L1=5, Lbce=0.2
    Hand-chosen in Eq. (9); the 0.2 coefficient on the binary cross-entropy for target word classification directly controls the strength of the LLM-label supervision during training.
  • Dynamic template update threshold and interval = confidence=0.8, interval=25
    Set in Appendix D; the dynamic template z_t is refreshed only when the predicted confidence exceeds 0.8 and after 25 frames. This directly affects the template feature f_t_Z used in the heatmap Eq. (5).
  • Learning rates and schedule = vision encoder 1e-5, others 1e-4, decay x0.1 at epoch 120
    Reported in Appendix D; standard but hand-specified and not ablated.
  • Image resolutions = template 128x128, search 256x256
    Fixed in Sec. 4.1; the number of search tokens N_x and the spatial resolution of the heatmap depend on these values.
  • Training epochs and instances = 150 epochs, 20k instances/epoch
    Set in Sec. 4.1; the final model is the one after 150 epochs, with LR decay at 120. The choice of 150 epochs is not ablated.
assumptions (4)
  • domain assumption Template-search similarity produces a valid target-context heatmap
    Eq. (5) defines ht as the mean over template tokens of the dot product between search and template features. The entire visual memory module treats ht as the probability that each search token is the target. If the template is stale or the similarity peaks on distractors, the memory units are corrupted. Location: Sec. 3.3, Eq. (5).
  • domain assumption GPT-4o annotations of target words are accurate and consistent
    Appendix A describes the LLM labeling pipeline and a manual check of only 50 sentences. The MLP (Eq. 1) is trained with these labels, and the 96.7% accuracy claim in Fig. 2 is measured against them, so any systematic label noise would propagate into the textual guidance module.
  • domain assumption The [CLS] token represents global visual context adequate for memory
    Section 3.3 uses the encoded [CLS] token as the global feature and as the initial memory unit. This is inherited from prior work (MemVLT, BERT) but not validated within this paper.
  • domain assumption The STARK dynamic template update keeps the template centered on the target
    The dynamic template z_t is updated according to the STARK strategy (Appendix D). Since Eq. (5) uses f_t_Z as the reference for the heatmap, a drift in the template would directly bias the target-context map and the memory. This is a known risk in self-updating trackers.

how reviews work

0 comments
Cite this review

Pith. "Pith review of ATCTrack: Aligning Target-Context Cues with Dynamic Target States for Robust Vision-Language Tracking." pith.science (2026). https://pith.science/paper/ZF5K747C

@misc{pith2026250719875,
  author       = {Pith},
  title        = {Pith review of: ATCTrack: Aligning Target-Context Cues with Dynamic Target States for Robust Vision-Language Tracking},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZF5K747C}},
  note         = {Machine review of arXiv:2507.19875}
}
read the original abstract

Vision-language tracking aims to locate the target object in the video sequence using a template patch and a language description provided in the initial frame. To achieve robust tracking, especially in complex long-term scenarios that reflect real-world conditions as recently highlighted by MGIT, it is essential not only to characterize the target features but also to utilize the context features related to the target. However, the visual and textual target-context cues derived from the initial prompts generally align only with the initial target state. Due to their dynamic nature, target states are constantly changing, particularly in complex long-term sequences. It is intractable for these cues to continuously guide Vision-Language Trackers (VLTs). Furthermore, for the text prompts with diverse expressions, our experiments reveal that existing VLTs struggle to discern which words pertain to the target or the context, complicating the utilization of textual cues. In this work, we present a novel tracker named ATCTrack, which can obtain multimodal cues Aligned with the dynamic target states through comprehensive Target-Context feature modeling, thereby achieving robust tracking. Specifically, (1) for the visual modality, we propose an effective temporal visual target-context modeling approach that provides the tracker with timely visual cues. (2) For the textual modality, we achieve precise target words identification solely based on textual content, and design an innovative context words calibration method to adaptively utilize auxiliary context words. (3) We conduct extensive experiments on mainstream benchmarks and ATCTrack achieves a new SOTA performance. The code and models will be released at: https://github.com/XiaokunFeng/ATCTrack.

Figures

Figures reproduced from arXiv: 2507.19875 by the authors.

Figure 1
Figure 1. (a) Limitations of initially given prompts(i.e., the tem￾plate patch and language description). Despite containing certain multimodal target-context cues, these static initial prompts are in￾tractable for continuously guiding the tracker in dynamic tracking sequences. Particularly, the objects corresponding to the target￾related text and context-related text can be ambiguous, which may mislead the tracker. (b) Our k… view at source ↗
Figure 2
Figure 2. (a) Comparison of the existing vision-text similarity-based method and ours in terms of target words classification accuracy. (b-c) Attention distribution maps for the target words during tracking using the existing method. In case (b), the model focuses on the word that corresponds to the tracking target, i.e., ‘plane,’ indicating that the tracker correctly understands the intent embedded in the text prompt, thereb… view at source ↗
Figure 3
Figure 3. (a) Framework of our proposed ATCTrack. Given the language description and template patches as references, ATCTrack locates the target in the search image at time t. The input is first encoded using Text and Vision Encoders. Subsequently, the Textual and Visual Target-Context Guidance Modules sequentially embed the aligned textual and visual cues into the search features. During this process, the Memory Storage Modu… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Qualitative comparison results of our tracker with other [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

100 extracted references · 49 canonical work pages

  1. [1]

    Visual prompting: Modifying pixel space to adapt pre-trained models

    Hyojin Bahng, Ali Jahanian, Swami Sankaranarayanan, and Phillip Isola. Visual prompting: Modifying pixel space to adapt pre-trained models. arXiv preprint arXiv:2203.17274, 3:11–12, 2022. 3

    arXiv 2022

  2. [2]

    Qwen technical report

    Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. Qwen technical report. arXiv preprint arXiv:2309.16609, 2023. 2, 4

    arXiv 2023

  3. [3]

    Ar- trackv2: Prompting autoregressive tracker where to look and how to describe

    Yifan Bai, Zeyang Zhao, Yihong Gong, and Xing Wei. Ar- trackv2: Prompting autoregressive tracker where to look and how to describe. arXiv preprint arXiv:2312.17133, 2023. 7

    arXiv 2023

  4. [4]

    Visual objects in context

    Moshe Bar. Visual objects in context. Nature Reviews Neu- roscience, 5(8):617–629, 2004. 4

    2004

  5. [5]

    Fully-convolutional siamese networks for object tracking

    Luca Bertinetto, Jack Valmadre, Joao F Henriques, Andrea Vedaldi, and Philip HS Torr. Fully-convolutional siamese networks for object tracking. In European conference on computer vision, pages 850–865. Springer, 2016. 7

    2016

  6. [6]

    Learning his- torical status prompt for accurate and robust visual tracking

    Wenrui Cai, Qingjie Liu, and Yunhong Wang. Learning his- torical status prompt for accurate and robust visual tracking. arXiv preprint arXiv:2311.02072, 2023. 7

    arXiv 2023

  7. [7]

    Hiptrack: Visual tracking with historical prompts

    Wenrui Cai, Qingjie Liu, and Yunhong Wang. Hiptrack: Visual tracking with historical prompts. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19258–19267, 2024. 6, 3

    2024

  8. [8]

    Robust object modeling for visual tracking

    Yidong Cai, Jie Liu, Jie Tang, and Gangshan Wu. Robust object modeling for visual tracking. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 9589–9600, 2023. 7

    2023

Show all 100 references
  1. [9]

    The relative con- tribution of scene context and target features to visual search in scenes

    Monica S Castelhano and Chelsea Heaven. The relative con- tribution of scene context and target features to visual search in scenes. Attention, Perception, & Psychophysics , 72(5): 1283–1297, 2010. 4

    2010

  2. [10]

    Back- bone is all your need: A simplified architecture for visual object tracking

    Boyu Chen, Peixia Li, Lei Bai, Lei Qiao, Qiuhong Shen, Bo Li, Weihao Gan, Wei Wu, and Wanli Ouyang. Back- bone is all your need: A simplified architecture for visual object tracking. In European Conference on Computer Vi- sion, pages 375–392. Springer, 2022. 7

    2022

  3. [11]

    Revealing the dark secrets of ex- tremely large kernel convnets on robustness

    Honghao Chen, Yurong Zhang, Xiaokun Feng, Xiangxiang Chu, and Kaiqi Huang. Revealing the dark secrets of ex- tremely large kernel convnets on robustness. arXiv preprint arXiv:2407.08972, 2024. 7

    2024 arXiv

  4. [12]

    Transformer tracking

    Xin Chen, Bin Yan, Jiawen Zhu, Dong Wang, Xiaoyun Yang, and Huchuan Lu. Transformer tracking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8126–8135, 2021. 7

    2021

  5. [13]

    Seqtrack: Sequence to sequence learning for visual ob- ject tracking

    Xin Chen, Houwen Peng, Dong Wang, Huchuan Lu, and Han Hu. Seqtrack: Sequence to sequence learning for visual ob- ject tracking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14572– 14581, 2023. 7

    2023

  6. [14]

    Sutrack: Towards simple and unified single object tracking

    Xin Chen, Ben Kang, Wanting Geng, Jiawen Zhu, Yi Liu, Dong Wang, and Huchuan Lu. Sutrack: Towards simple and unified single object tracking. arXiv preprint arXiv:2412.19138, 2024. 6, 8, 5, 7

    2024 arXiv

  7. [15]

    Siamese box adaptive network for visual tracking

    Zedu Chen, Bineng Zhong, Guorong Li, Shengping Zhang, and Rongrong Ji. Siamese box adaptive network for visual tracking. In CVPR, pages 6668–6677, 2020. 7

    2020

  8. [16]

    Mixformer: End-to-end tracking with iterative mixed atten- tion

    Yutao Cui, Cheng Jiang, Limin Wang, and Gangshan Wu. Mixformer: End-to-end tracking with iterative mixed atten- tion. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition , pages 13608–13618,

  9. [17]

    Bert: Pre-training of deep bidirectional transformers for language understanding

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. 3, 4, 5

    2018 arXiv

  10. [18]

    Scaling recti- fied flow transformers for high-resolution image synthesis

    Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas M ¨uller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling recti- fied flow transformers for high-resolution image synthesis. In Forty-first international conference on mach...

  11. [19]

    Lasot: A high-quality benchmark for large-scale single ob- ject tracking

    Heng Fan, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, Hexin Bai, Yong Xu, Chunyuan Liao, and Haibin Ling. Lasot: A high-quality benchmark for large-scale single ob- ject tracking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pag...

    2019

  12. [20]

    Lasot: A high-quality large-scale single object tracking benchmark

    Heng Fan, Hexin Bai, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, Harshit, Mingzhen Huang, Juehuan Liu, et al. Lasot: A high-quality large-scale single object tracking benchmark. International Journal of Computer Vision, 129: 439–461, 2021. 2, 6, 1, 5, 7

    2021

  13. [21]

    Robust visual object tracking with natural language region proposal network

    Qi Feng, Vitaly Ablavsky, Qinxun Bai, and Stan Sclaroff. Robust visual object tracking with natural language region proposal network. arXiv preprint arXiv:1912.02048, 1(7):8,

    1912 arXiv

  14. [22]

    Real-time visual object tracking with natural lan- guage description

    Qi Feng, Vitaly Ablavsky, Qinxun Bai, Guorong Li, and Stan Sclaroff. Real-time visual object tracking with natural lan- guage description. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 700– 709, 2020. 5

    2020

  15. [23]

    Siamese natural language tracker: Tracking by natural lan- guage descriptions with siamese trackers

    Qi Feng, Vitaly Ablavsky, Qinxun Bai, and Stan Sclaroff. Siamese natural language tracker: Tracking by natural lan- guage descriptions with siamese trackers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5847–5856, 2021. 3, 5

    2021

  16. [24]

    Enhanc- ing vision-language tracking by effectively converting tex- tual cues into visual cues

    Xiaokun Feng, Dailing Zhang, Shiyu Hu, Xuchen Li, Meiqi Wu, Jing Zhang, Xiaotang Chen, and Kaiqi Huang. Enhanc- ing vision-language tracking by effectively converting tex- tual cues into visual cues. arXiv preprint arXiv:2412.19648,

    arXiv

  17. [25]

    Memvlt: Vision- language tracking with adaptive memory-based prompts

    Xiaokun Feng, Xuchen Li, Shiyu Hu, Dailing Zhang, Jing Zhang, Xiaotang Chen, Kaiqi Huang, et al. Memvlt: Vision- language tracking with adaptive memory-based prompts. Advances in Neural Information Processing Systems , 37: 14903–14933, 2025. 3, 4, 5, 6, 7, 8

    2025

  18. [26]

    Narrlv: Towards a comprehensive narrative-centric evaluation for long video generation models

    Xiaokun Feng, Haiming Yu, Meiqi Wu, Shiyu Hu, Jintao Chen, Chen Zhu, Jiahong Wu, Xiangxiang Chu, and Kaiqi Huang. Narrlv: Towards a comprehensive narrative-centric evaluation for long video generation models. arXiv preprint arXiv:2507.11245, 2025. 7

    2025

  19. [27]

    Cstrack: Enhancing rgb-x tracking via compact spatiotemporal fea- tures

    Xiaokun Feng, Dailing Zhang, Shiyu Hu, Xuchen Li, Meiqi Wu, Jing Zhang, Xiaotang Chen, and Kaiqi Huang. Cstrack: Enhancing rgb-x tracking via compact spatiotemporal fea- tures. arXiv preprint arXiv:2505.19434, 2025. 7

    2025 arXiv

  20. [28]

    Aiatrack: Attention in attention for trans- former visual tracking

    Shenyuan Gao, Chunluan Zhou, Chao Ma, Xinggang Wang, and Junsong Yuan. Aiatrack: Attention in attention for trans- former visual tracking. In European Conference on Com- puter Vision, pages 146–164. Springer, 2022. 7

    2022

  21. [29]

    Generalized relation modeling for transformer tracking

    Shenyuan Gao, Chunluan Zhou, and Jun Zhang. Generalized relation modeling for transformer tracking. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18686–18695, 2023. 7

    2023

  22. [30]

    Divert more attention to vision-language tracking

    Mingzhe Guo, Zhipeng Zhang, Heng Fan, and Liping Jing. Divert more attention to vision-language tracking. In Pro- ceedings of the Advances in Neural Information Processing Systems, pages 4446–4460, 2022. 2, 5

    2022

  23. [31]

    Learning target-aware rep- resentation for visual tracking via informative interactions

    Mingzhe Guo, Zhipeng Zhang, Heng Fan, Liping Jing, Yilin Lyu, Bing Li, and Weiming Hu. Learning target-aware rep- resentation for visual tracking via informative interactions. arXiv preprint arXiv:2201.02526, 2022. 7

    2022 arXiv

  24. [32]

    Onetracker: Unifying visual object tracking with foundation models and efficient tuning

    Lingyi Hong, Shilin Yan, Renrui Zhang, Wanyun Li, Xinyu Zhou, Pinxue Guo, Kaixun Jiang, Yiting Chen, Jinglun Li, Zhaoyu Chen, et al. Onetracker: Unifying visual object tracking with foundation models and efficient tuning. InPro- ceedings of the IEEE/CVF Conference on Computer ...

    2024

  25. [33]

    A multi-modal global instance tracking benchmark (mgit): Better locating target in complex spatio-temporal and causal relationship

    Shiyu Hu, Dailing Zhang, Meiqi Wu, Xiaokun Feng, Xuchen Li, Xin Zhao, and Kaiqi Huang. A multi-modal global instance tracking benchmark (mgit): Better locating target in complex spatio-temporal and causal relationship. In the 37th Conference on Neural Information Processing Sy...

    2023

  26. [34]

    Global instance tracking: Locating target more like humans

    Shiyu Hu, Xin Zhao, Lianghua Huang, and Kaiqi Huang. Global instance tracking: Locating target more like humans. IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 45(1):576–592, 2023. 7

    2023

  27. [35]

    Sotverse: A user- defined task space of single object tracking

    Shiyu Hu, Xin Zhao, and Kaiqi Huang. Sotverse: A user- defined task space of single object tracking. International Journal of Computer Vision, 132:872–930, 2024. 2

    2024

  28. [36]

    Got-10k: A large high-diversity benchmark for generic object tracking in the wild

    Lianghua Huang, Xin Zhao, and Kaiqi Huang. Got-10k: A large high-diversity benchmark for generic object tracking in the wild. IEEE transactions on pattern analysis and machine intelligence, 43(5):1562–1577, 2019. 7

    2019

  29. [37]

    Gpt-4o system card

    Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perel- man, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Weli- hinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276, 2024. 1

    2024 arXiv

  30. [38]

    Hunyuanvideo: A systematic framework for large video generative models

    Weijie Kong, Qi Tian, Zijian Zhang, Rox Min, Zuozhuo Dai, Jin Zhou, Jiangfeng Xiong, Xin Li, Bo Wu, Jianwei Zhang, et al. Hunyuanvideo: A systematic framework for large video generative models. arXiv preprint arXiv:2412.03603, 2024. 5

    2024 arXiv

  31. [39]

    Zoomtrack: Target-aware non-uniform resizing for efficient visual tracking

    Yutong Kou, Jin Gao, Bing Li, Gang Wang, Weiming Hu, Yizheng Wang, and Liang Li. Zoomtrack: Target-aware non-uniform resizing for efficient visual tracking. Advances in Neural Information Processing Systems, 36:50959–50977,

  32. [40]

    Multi- modal data fusion: an overview of methods, challenges, and prospects

    Dana Lahat, T ¨ulay Adali, and Christian Jutten. Multi- modal data fusion: an overview of methods, challenges, and prospects. Proceedings of the IEEE, 103(9):1449–1477,

  33. [41]

    Cornernet: Detecting objects as paired keypoints

    Hei Law and Jia Deng. Cornernet: Detecting objects as paired keypoints. In Proceedings of the European confer- ence on computer vision (ECCV), pages 734–750, 2018. 6

    2018

  34. [42]

    The power of scale for parameter-efficient prompt tuning.arXiv preprint arXiv:2104.08691, 2021

    Brian Lester, Rami Al-Rfou, and Noah Constant. The power of scale for parameter-efficient prompt tuning.arXiv preprint arXiv:2104.08691, 2021. 3

    2021 arXiv

  35. [43]

    SiamRPN++: Evolution of siamese visual tracking with very deep networks

    Bo Li, Wei Wu, Qiang Wang, Fangyi Zhang, Junliang Xing, and Junjie Yan. SiamRPN++: Evolution of siamese visual tracking with very deep networks. In CVPR, pages 4282– 4291, 2019. 7

    2019

  36. [44]

    Dtllm-vlt: Diverse text generation for visual language tracking based on llm

    Xuchen Li, Xiaokun Feng, Shiyu Hu, Meiqi Wu, Dailing Zhang, Jing Zhang, and Kaiqi Huang. Dtllm-vlt: Diverse text generation for visual language tracking based on llm. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7283–7292, 2024. 2

    2024

  37. [45]

    Dtvlt: A multi-modal diverse text benchmark for visual language tracking based on llm

    Xuchen Li, Shiyu Hu, Xiaokun Feng, Dailing Zhang, Meiqi Wu, Jing Zhang, and Kaiqi Huang. Dtvlt: A multi-modal diverse text benchmark for visual language tracking based on llm. arXiv preprint arXiv:2410.02492, 2024. 7

    2024 arXiv

  38. [46]

    How texts help? a fine- grained evaluation to reveal the role of language in vision- language tracking

    Xuchen Li, Shiyu Hu, Xiaokun Feng, Dailing Zhang, Meiqi Wu, Jing Zhang, and Kaiqi Huang. How texts help? a fine- grained evaluation to reveal the role of language in vision- language tracking. arXiv preprint arXiv:2411.15600, 2024

    2024 arXiv

  39. [47]

    Visual language tracking with multi-modal interaction: A robust benchmark

    Xuchen Li, Shiyu Hu, Xiaokun Feng, Dailing Zhang, Meiqi Wu, Jing Zhang, and Kaiqi Huang. Visual language tracking with multi-modal interaction: A robust benchmark. arXiv preprint arXiv:2409.08887, 2024. 7

    2024 arXiv

  40. [48]

    Cross- modal target retrieval for tracking by natural language

    Yihao Li, Jun Yu, Zhongpeng Cai, and Yuwen Pan. Cross- modal target retrieval for tracking by natural language. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition, pages 4931–4940, 2022. 2

    2022

  41. [49]

    Tracking by natural language specification

    Zhenyang Li, Ran Tao, Efstratios Gavves, Cees GM Snoek, and Arnold WM Smeulders. Tracking by natural language specification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 6495– 6503, 2017. 1, 2, 3, 4, 7

    2017

  42. [50]

    Tracking meets lora: Faster training, larger model, stronger performance

    Liting Lin, Heng Fan, Zhipeng Zhang, Yaowei Wang, Yong Xu, and Haibin Ling. Tracking meets lora: Faster training, larger model, stronger performance. In ECCV, 2024. 7

    2024

  43. [51]

    Vmbench: A benchmark for perception-aligned video motion generation

    Xinran Ling, Chen Zhu, Meiqi Wu, Hangyu Li, Xiaokun Feng, Cundian Yang, Aiming Hao, Jiashu Zhu, Jiahong Wu, and Xiangxiang Chu. Vmbench: A benchmark for perception-aligned video motion generation. arXiv preprint arXiv:2503.10076, 2025. 7

    2025 arXiv

  44. [52]

    Roberta: A robustly optimized bert pretraining approach

    Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettle- moyer, and Veselin Stoyanov. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692,

    1907 arXiv

  45. [53]

    Decoupled weight decay regularization

    Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 7, 4

    2017 arXiv

  46. [54]

    Tracking by natural language specification with long short-term context decoupling

    Ding Ma and Xiangqian Wu. Tracking by natural language specification with long short-term context decoupling. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14012–14021, 2023. 6, 5

    2023

  47. [55]

    Step-video-t2v technical re- port: The practice, challenges, and future of video founda- tion model

    Guoqing Ma, Haoyang Huang, Kun Yan, Liangyu Chen, Nan Duan, Shengming Yin, Changyi Wan, Ranchen Ming, Xi- aoniu Song, Xing Chen, et al. Step-video-t2v technical re- port: The practice, challenges, and future of video founda- tion model. arXiv preprint arXiv:2502.10248, 2025. 5

    2025 arXiv

  48. [56]

    Unifying visual and vision-language tracking via contrastive learning

    Yinchao Ma, Yuyang Tang, Wenfei Yang, Tianzhu Zhang, Jinpeng Zhang, and Mengxue Kang. Unifying visual and vision-language tracking via contrastive learning. In Pro- ceedings of the AAAI Conference on Artificial Intelligence , pages 4107–4116, 2024. 3, 6, 7, 5

    2024

  49. [57]

    Generation and comprehension of unambiguous object descriptions

    Junhua Mao, Jonathan Huang, Alexander Toshev, Oana Camburu, Alan L Yuille, and Kevin Murphy. Generation and comprehension of unambiguous object descriptions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 11–20, 2016. 7, 1

    2016

  50. [58]

    Textual tokens classification for multi-modal alignment in vision-language tracking

    Zhongjie Mao, Yucheng Wang, Xi Chen, and Jia Yan. Textual tokens classification for multi-modal alignment in vision-language tracking. In ICASSP 2024-2024 IEEE Inter- national Conference on Acoustics, Speech and Signal Pro- cessing (ICASSP), pages 8025–8029. IEEE, 2024. 2, 4, 1, 3, 5

    2024

  51. [59]

    Learning target candidate association to keep track of what not to track

    Christoph Mayer, Martin Danelljan, Danda Pani Paudel, and Luc Van Gool. Learning target candidate association to keep track of what not to track. In ICCV, pages 13444–13454,

  52. [60]

    Trackingnet: A large-scale dataset and benchmark for object tracking in the wild

    Matthias Muller, Adel Bibi, Silvio Giancola, Salman Al- subaihi, and Bernard Ghanem. Trackingnet: A large-scale dataset and benchmark for object tracking in the wild. In Proceedings of the European conference on computer vision (ECCV), pages 300–317, 2018. 7

    2018

  53. [61]

    Vast- track: Vast category visual object tracking.Advances in Neu- ral Information Processing Systems , 37:130797–130818,

    Liang Peng, Junyuan Gao, Xinran Liu, Weihong Li, Shaohua Dong, Zhipeng Zhang, Heng Fan, and Libo Zhang. Vast- track: Vast category visual object tracking.Advances in Neu- ral Information Processing Systems , 37:130797–130818,

  54. [62]

    Faster r-cnn: Towards real-time object detection with region proposal networks

    Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information process- ing systems, 28, 2015. 3, 5

    2015

  55. [63]

    Generalized in- tersection over union: A metric and a loss for bounding box regression

    Hamid Rezatofighi, Nathan Tsoi, JunYoung Gwak, Amir Sadeghian, Ian Reid, and Silvio Savarese. Generalized in- tersection over union: A metric and a loss for bounding box regression. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 658–666,

  56. [64]

    Generating semantically precise scene graphs from textual descriptions for improved image retrieval

    Sebastian Schuster, Ranjay Krishna, Angel Chang, Li Fei- Fei, and Christopher D Manning. Generating semantically precise scene graphs from textual descriptions for improved image retrieval. In Proceedings of the fourth workshop on vision and language, pages 70–80, 2015. 8, 3

    2015

  57. [65]

    Context-aware integration of lan- guage and visual references for natural language tracking

    Yanyan Shao, Shuting He, Qi Ye, Yuchao Feng, Wenhan Luo, and Jiming Chen. Context-aware integration of lan- guage and visual references for natural language tracking. arXiv preprint arXiv:2403.19975, 2024. 2, 3, 4, 6, 1, 5

    2024 arXiv

  58. [66]

    Explicit visual prompts for vi- sual object tracking

    Liangtao Shi, Bineng Zhong, Qihua Liang, Ning Li, Sheng- ping Zhang, and Xianxian Li. Explicit visual prompts for vi- sual object tracking. arXiv preprint arXiv:2401.03142, 2024. 6, 7

    2024 arXiv

  59. [67]

    Chat- tracker: Enhancing visual tracking performance via chatting with multimodal large language model

    Yiming Sun, Fan Yu, Shaoxiang Chen, Yu Zhang, Junwei Huang, Yang Li, Chenhui Li, and Changbo Wang. Chat- tracker: Enhancing visual tracking performance via chatting with multimodal large language model. Advances in Neural Information Processing Systems, 37:39303–39324, 2025. 6, 5

    2025

  60. [68]

    What makes for good views for contrastive learning? Advances in neural informa- tion processing systems, 33:6827–6839, 2020

    Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola. What makes for good views for contrastive learning? Advances in neural informa- tion processing systems, 33:6827–6839, 2020. 3

    2020

  61. [69]

    Fast-itpn: Integrally pre- trained transformer pyramid network with token migration

    Yunjie Tian, Lingxi Xie, Jihao Qiu, Jianbin Jiao, Yaowei Wang, Qi Tian, and Qixiang Ye. Fast-itpn: Integrally pre- trained transformer pyramid network with token migration. IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 2024. 6, 8

    2024

  62. [70]

    Llama: Open and efficient foundation language models

    Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timoth´ee Lacroix, Baptiste Rozi`ere, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023. 2, 4, 1

    2023 arXiv

  63. [71]

    Attention is all you need.Proceedings of the Ad- vances in Neural Information Processing Systems, 30, 2017

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszko- reit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need.Proceedings of the Ad- vances in Neural Information Processing Systems, 30, 2017. 5, 4

    2017

  64. [72]

    Temporal adaptive rgbt tracking with modality prompt

    Hongyu Wang, Xiaotao Liu, Yifan Li, Meng Sun, Dian Yuan, and Jing Liu. Temporal adaptive rgbt tracking with modality prompt. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 5436–5444, 2024. 3

    2024

  65. [73]

    Transformer meets tracker: Exploiting temporal context for robust visual tracking

    Ning Wang, Wengang Zhou, Jie Wang, and Houqiang Li. Transformer meets tracker: Exploiting temporal context for robust visual tracking. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition , pages 1571–1580, 2021. 3, 8, 5

    2021

  66. [74]

    Unified transformer with isomorphic branches for natural language tracking

    Rong Wang, Zongheng Tang, Qianli Zhou, Xiaoqian Liu, Tianrui Hui, Quange Tan, and Si Liu. Unified transformer with isomorphic branches for natural language tracking. IEEE Transactions on Circuits and Systems for Video Tech- nology, 2023. 4, 5

    2023

  67. [75]

    Describe and attend to track: Learning natural language guided structural representation and visual attention for object tracking

    Xiao Wang, Chenglong Li, Rui Yang, Tianzhu Zhang, Jin Tang, and Bin Luo. Describe and attend to track: Learning natural language guided structural representation and visual attention for object tracking. arXiv preprint arXiv:1811.10014, 2018. 5

    2018 arXiv

  68. [76]

    Towards more flexible and accurate object tracking with natural language: Algo- rithms and benchmark

    Xiao Wang, Xiujun Shu, Zhipeng Zhang, Bo Jiang, Yaowei Wang, Yonghong Tian, and Feng Wu. Towards more flexible and accurate object tracking with natural language: Algo- rithms and benchmark. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition ,...

    2021

  69. [77]

    Autoregressive visual tracking

    Xing Wei, Yifan Bai, Yongchao Zheng, Dahu Shi, and Yi- hong Gong. Autoregressive visual tracking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 9697–9706, 2023. 7

    2023

  70. [78]

    Dropmae: Masked autoen- coders with spatial-attention dropout for tracking tasks

    Qiangqiang Wu, Tianyu Yang, Ziquan Liu, Baoyuan Wu, Ying Shan, and Antoni B Chan. Dropmae: Masked autoen- coders with spatial-attention dropout for tracking tasks. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition, pages 14561–14571, 2023. 7

    2023

  71. [79]

    Object track- ing benchmark

    Yi Wu, Jongwoo Lim, and Ming-Hsuan Yang. Object track- ing benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(09):1834–1848, 2015. 7

    2015

  72. [80]

    Au- toregressive queries for adaptive tracking with spatio- temporaltransformers

    Jinxia Xie, Bineng Zhong, Zhiyi Mo, Shengping Zhang, Liangtao Shi, Shuxiang Song, and Rongrong Ji. Au- toregressive queries for adaptive tracking with spatio- temporaltransformers. arXiv preprint arXiv:2403.10574 ,

    arXiv

  73. [81]

    Less is more: Token context-aware learning for object tracking

    Chenlong Xu, Bineng Zhong, Qihua Liang, Yaozong Zheng, Guorong Li, and Shuxiang Song. Less is more: Token context-aware learning for object tracking. InProceedings of the AAAI Conference on Artificial Intelligence, pages 8824– 8832, 2025. 2

    2025

  74. [82]

    Learning spatio-temporal transformer for vi- sual tracking

    Bin Yan, Houwen Peng, Jianlong Fu, Dong Wang, and Huchuan Lu. Learning spatio-temporal transformer for vi- sual tracking. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10448–10457, 2021. 5, 6, 8, 4, 7

    2021

  75. [83]

    Learning spatio-temporal transformer for vi- sual tracking

    Bin Yan, Houwen Peng, Jianlong Fu, Dong Wang, and Huchuan Lu. Learning spatio-temporal transformer for vi- sual tracking. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10448–10457, 2021. 3

    2021

  76. [84]

    Foreground-background distribution mod- eling transformer for visual object tracking

    Dawei Yang, Jianfeng He, Yinchao Ma, Qianjin Yu, and Tianzhu Zhang. Foreground-background distribution mod- eling transformer for visual object tracking. In Proceedings of the IEEE/CVF International Conference on Computer Vi- sion, pages 10117–10127, 2023. 7

    2023

  77. [85]

    Grounding-tracking-integration

    Zhengyuan Yang, Tushar Kumar, Tianlang Chen, Jingsong Su, and Jiebo Luo. Grounding-tracking-integration. IEEE Transactions on Circuits and Systems for Video Technology, 31(9):3433–3443, 2021. 3, 7, 5

    2021

  78. [86]

    Joint feature learning and relation modeling for tracking: A one-stream framework

    Botao Ye, Hong Chang, Bingpeng Ma, Shiguang Shan, and Xilin Chen. Joint feature learning and relation modeling for tracking: A one-stream framework. In Proceedings of the European Conference on Computer Vision, pages 341–357,

  79. [87]

    All in one: Exploring uni- fied vision-language tracking with multi-modal alignment

    Chunhui Zhang, Xin Sun, Yiqian Yang, Li Liu, Qiong Liu, Xi Zhou, and Yanfeng Wang. All in one: Exploring uni- fied vision-language tracking with multi-modal alignment. In Proceedings of the 31st ACM International Conference on Multimedia, pages 5552–5561, 2023. 3, 6, 7, 5

    2023

  80. [88]

    Beyond accuracy: Tracking more like human via visual search

    Dailing Zhang, Shiyu Hu, Xiaokun Feng, Xuchen Li, Jing Zhang, Kaiqi Huang, et al. Beyond accuracy: Tracking more like human via visual search. Advances in Neural Informa- tion Processing Systems, 37:2629–2662, 2025. 3, 5

    2025

  81. [89]

    One-stream stepwise decreas- ing for vision-language tracking

    Guangtong Zhang, Bineng Zhong, Qihua Liang, Zhiyi Mo, Ning Li, and Shuxiang Song. One-stream stepwise decreas- ing for vision-language tracking. IEEE Transactions on Cir- cuits and Systems for Video Technology, 2024. 3, 1, 5

    2024

  82. [90]

    Hivit: A simpler and more efficient design of hierarchical vision transformer

    Xiaosong Zhang, Yunjie Tian, Lingxi Xie, Wei Huang, Qi Dai, Qixiang Ye, and Qi Tian. Hivit: A simpler and more efficient design of hierarchical vision transformer. In The Eleventh International Conference on Learning Representa- tions, 2022. 6, 8

    2022

  83. [91]

    Transformer vision-language tracking via proxy token guided cross-modal fusion

    Haojie Zhao, Xiao Wang, Dong Wang, Huchuan Lu, and Xi- ang Ruan. Transformer vision-language tracking via proxy token guided cross-modal fusion. Pattern Recognition Let- ters, 168:10–16, 2023. 5

    2023

  84. [92]

    Biodrone: A bionic drone-based single object tracking benchmark for robust vision

    Xin Zhao, Shiyu Hu, Yipei Wang, Jing Zhang, Yimin Hu, Rongshuai Liu, Haibin Ling, Yin Li, Renshu Li, Kun Liu, and Jiadong Li. Biodrone: A bionic drone-based single object tracking benchmark for robust vision. International Journal of Computer Vision, 132:1659–1684, 2024. 2

    2024

  85. [93]

    Leveraging local and global cues for visual tracking via parallel interaction network

    Yaozong Zheng, Bineng Zhong, Qihua Liang, Zhenjun Tang, Rongrong Ji, and Xianxian Li. Leveraging local and global cues for visual tracking via parallel interaction network. IEEE Transactions on Circuits and Systems for Video Tech- nology, 33(4):1671–1683, 2022. 2

    2022

  86. [94]

    Towards unified token learn- ing for vision-language tracking

    Yaozong Zheng, Bineng Zhong, Qihua Liang, Guorong Li, Rongrong Ji, and Xianxian Li. Towards unified token learn- ing for vision-language tracking. IEEE Transactions on Cir- cuits and Systems for Video Technology, 2023. 3, 5, 6, 7, 4, 8

    2023

  87. [95]

    Odtrack: Online dense temporal token learning for visual tracking

    Yaozong Zheng, Bineng Zhong, Qihua Liang, Zhiyi Mo, Shengping Zhang, and Xianxian Li. Odtrack: Online dense temporal token learning for visual tracking. arXiv preprint arXiv:2401.01686, 2024. 3, 7

    2024 arXiv

  88. [96]

    Decoupled spatio-temporal consistency learning for self-supervised tracking

    Yaozong Zheng, Bineng Zhong, Qihua Liang, Ning Li, and Shuxiang Song. Decoupled spatio-temporal consistency learning for self-supervised tracking. In Proceedings of the AAAI Conference on Artificial Intelligence , pages 10635– 10643, 2025. 3

    2025

  89. [97]

    Conditional prompt learning for vision-language mod- els

    Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Conditional prompt learning for vision-language mod- els. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition , pages 16816–16825,

  90. [98]

    Joint vi- sual grounding and tracking with natural language specifica- tion

    Li Zhou, Zikun Zhou, Kaige Mao, and Zhenyu He. Joint vi- sual grounding and tracking with natural language specifica- tion. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition , pages 23151–23160,

  91. [100]

    the ironman in red flying in the sky

    and TrDiMP [73]. We apply RoI processing to the search features f t X using the predicted bounding box scaled by 1.5 to obtain localized search features f t X ′ ∈ R36×D. Subsequently, the visual memory representation process is implemented through the following computations: f...

  92. [2023]

    plane”. In the corresponding Attl heatmap, the target word “plane

    2, 3, 6, 7, 1, 4, 5, 8 ATCTrack: Aligning Target-Context Cues with Dynamic Target States for Robust Vision-Language Tracking Supplementary Material A. Target Words Annotation Pipeline Given the inherently flexible and diverse nature of tex- tual descriptions, it is challenging...

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.