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REVIEW 2 major objections 8 minor 49 references

DARTer: Dynamic Adaptive Representation Tracker for Nighttime UAV Tracking

T0 review · 2 major / 8 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read DARTer claims that fusing static and dynamic template features and adaptively skipping ViT layers yields state-of-the-art nighttime UAV tracking on five benchmarks, with a 6.3% precision gain on NAT2021-L at over 74 FPS.

desk verdict Solid incremental results for nighttime UAV tracking, but the DFA gating mechanism's training recipe is missing and that gap is load-bearing. read the letter →

arxiv 2505.00752 v2 pith:WMYA73F3 submitted 2025-05-01 cs.CV cs.AI

Xuzhao Li , Xuchen Li , Shiyu Hu This is my paper · ORCID
classification cs.CVcs.AI
keywords nighttimeUAVtrackingdynamicfeaturefusionadaptivecomputationVisionTransformertemplatematchinglow-lightreal-time
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 paper is trying to establish that nighttime UAV tracking does not require a separate light enhancer or complex domain adaptation. It proposes DARTer, a single-stream tracker whose two modules, the Dynamic Feature Blender and the Dynamic Feature Activator, handle dark multi-perspective templates and skip unneeded transformer layers. The authors report state-of-the-art results across five nighttime benchmarks, including a 6.3% precision improvement on NAT2021-L, while running at over 74 frames per second. If true, this matters because it would give real-time UAV systems a way to track at night without paying the computational cost of enhancement networks.

What carries the argument

The load-bearing machinery is the pair of modules: DFB, a cross-attention fusion of static and dynamic template features also applied to overlapping patches, and DFA, a linear-plus-convolution gating head that maps the tokens of the previous ViT layer to a probability and skips the next layer when the probability is below the threshold 0.3. DFB makes the representation robust to viewpoint change; DFA converts fixed-depth feature extraction into adaptive computation. The single loss combining softmax cross-entropy and SIoU is deliberately simple, so the claimed gains rest on these two modules rather than on auxiliary losses.

What would settle it

Run the exact training recipe with the DFA gating branch's gradients removed, or with the gate forced to always active, and compare NAT2021-L precision; if performance does not drop, the reported gains are not caused by adaptive layer skipping.

Watch

Extended reading notes

Core claim

The central discovery claimed is that fusing static and dynamic template features under dark conditions, then adaptively activating only the needed ViT layers, produces both higher tracking accuracy and lower computation than prior nighttime trackers. The Dynamic Feature Blender (DFB) performs cross-attention between static and dynamic template tokens, concatenates the results, and repeats the operation on overlapped patches, producing a night-fused representation. The Dynamic Feature Activator (DFA) computes an activation probability from the previous layer's tokens and skips the next ViT block when the probability falls below 0.3, so the network spends computation only where features warrant it. With this design, DARTer reports top precision, normalized precision, and AUC on NAT2024-1, NAT2021, UAVDark135, NAT2021-L, and DarkTrack2021, reaching 85.2% precision on NAT2024-1, 64.9% precision on NAT2021-L, and over 74 FPS with 80.9M parameters.

Load-bearing premise

The whole accuracy-and-efficiency story assumes the network can actually learn which transformer layers to skip, but the paper never explains how the skip decision is trained given that the threshold comparison is a hard, non-differentiable choice.

Editorial extensions

If this is right

  • If the results hold, a nighttime UAV tracker can be accurate without a separate low-light enhancement network, avoiding that extra compute.
  • The 6.3% precision gain on NAT2021-L over the prior best would make DARTer the new reference for that benchmark's hard nighttime cases.
  • A 74+ FPS runtime with 80.9M parameters means the approach fits real-time use on a single consumer GPU, unlike slower SOTA trackers such as the 35 FPS darkness-prompted model it outperforms.
  • The ablations attribute 1.9 to 3.7 point gains on NAT2024-1 to the DFB and DFA individually, so each module carries its own contribution to the reported improvement.

Reading between the lines

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

  • The same static-plus-dynamic template fusion mechanism could plausibly improve daytime multi-template trackers, since nothing about DFB is inherently night-specific; the paper does not test that setting.
  • A natural next test is to apply DARTer's DFA to a larger ViT backbone; if the skip policy transfers, the efficiency gain should grow with depth, which the paper does not explore.
  • The fixed-interval dynamic template update suggests an online extension where update frequency is itself learned or scheduled, a direction the paper leaves implicit.
  • Because the paper does not describe how the discrete thresholding in DFA is trained, a reader should treat the speed-accuracy balance as contingent on the gating being actually learnable, not just on the reported numbers.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 8 minor

Summary. The paper presents DARTer, a single-stream transformer-based tracker for nighttime UAV tracking. The method introduces a Dynamic Feature Blender (DFB) that fuses static and dynamic template features via cross-attention, and a Dynamic Feature Activator (DFA) that skips Vision Transformer blocks based on an activation probability derived from the current tokens. Training uses a weighted combination of softmax cross-entropy and SIoU losses. DARTer is evaluated on five nighttime benchmarks and reported to outperform previous state-of-the-art methods, with a 6.3% precision improvement on NAT2021-L and a speed of over 74 FPS with 80.9M parameters.

Significance. If the reported results are reproducible, DARTer would provide a useful accuracy-efficiency trade-off for nighttime UAV tracking, with consistent improvements over a wide range of baselines on five benchmarks. The paper also provides an ablation of both proposed components and demonstrates real-time inference. The main significance is conditional on resolving the ambiguous training procedure for the DFA; until then, the attribution of the improvement to the adaptive gating mechanism is not supported by the manuscript as written.

major comments (2)
  1. [Section 2.2, Eq. (4) and Section 2.3] The DFA's hard decision (execute the i-th ViT block only if p_i > beta, otherwise skip) is non-differentiable. The total loss in Section 2.3 is a function only of the final prediction head, and no straight-through estimator, Gumbel-softmax relaxation, REINFORCE baseline, or auxiliary gating loss is described. Consequently, the parameters of the linear layer L and convolution Conv in Eq. (4) receive zero gradient almost everywhere during end-to-end training, and the 1.8-point precision gain attributed to DFA in Table 4 cannot be explained by the stated training procedure. Please specify the exact optimization scheme for the DFA, including any relaxation or estimator, and provide an experiment showing that the gate parameters actually change during training (e.g., statistics of p_i before and after training).
  2. [Section 2.2, Eq. (4)] The definition of the feature extraction vector v is underspecified. The text says v belongs to the standard normal distribution N(0,1), but it is not stated whether v is a learned parameter, a fixed random projection, or re-sampled at each forward pass. This matters because r_i = v * t^{i-1}(f_D) determines the activation probability, and if v is randomly sampled, the DFA input is stochastic even at test time. Please clarify the role and training status of v.
minor comments (8)
  1. [Table 3] The header of the 'Source' column incorrectly reads 'NAT2021-L'; it should read 'DarkTrack2021'.
  2. [Section 3.1] The image sizes for search and template are stated as 128×128 and 256×256, respectively, which is the opposite of the usual convention (search region larger than template). Please confirm the correct values.
  3. [Section 3.2, first paragraph] The proposed tracker is referred to as 'DFTrack' instead of 'DARTer'.
  4. [Section 2.2] The sentence 'To avoid the situation where all blocks are not activated, we perform feature activation calculations on all blocks except the first ViT block' is ambiguous; it should simply state that the first ViT block always executes and the activation probability is computed for the remaining blocks.
  5. [Section 3.1] The learning rate schedule ('after 120 epochs, the learning rate decays at a rate of 10%') is ambiguous; please specify whether this is a per-epoch decay factor, a one-time decay, or a cosine schedule.
  6. [Sections 2.2 and 3.1] The dynamic template update interval is said to be fixed but its value is never given; please provide it.
  7. [Table 4 and Section 3.3] The text reports that the DFB increases the success score by 1.95%, but the table shows a change of 1.9 percentage points (or a relative increase of about 3.05%); please align the reported percentages.
  8. [General formatting] The paper uses ACM template placeholders ('Conference acronym XX', 'Woodstock NY', 2018) in the header; these should be replaced with the actual venue information.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: DARTer's claimed gains are empirical benchmark results, not derived from its own definitions or fitted to the test sets.

full rationale

The paper's central claim is that DARTer outperforms prior trackers on five nighttime UAV benchmarks. This is supported by reported evaluation scores, not by a derivation that reduces to its own inputs. The DFB fuses static and dynamic template features via cross-attention, and the DFA gates ViT blocks using p_i = sigma(L(r_i)+Conv(r_i)) with a hard threshold beta; neither component is defined in terms of the reported benchmark metrics, and no parameter is fitted to the test-set outcomes. Hyperparameters such as beta=0.3 and lambda1=lambda2=2 are stated as implementation choices, and even if they were tuned on a validation split, that would not make the evaluated test numbers equal to the training inputs by construction. The self-citations in the reference list ([11,17,25-27]) are prior works by the authors, but they are not used as load-bearing justifications for the performance claims, nor are they invoked to forbid alternative designs. The absence of a described gradient path for the hard-threshold DFA gate is a potential correctness or reproducibility concern, not circularity, because it does not turn the claimed results into an equivalence between the output and the input. The paper is self-contained against external benchmarks, so the circularity score is 0.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The central claim rests on two hand-chosen hyperparameters (beta, lambda) and two domain assumptions about feature fusion and layer skipping that are tested empirically but not derived. No new physical entities are introduced.

free parameters (2)
  • Activation threshold beta = 0.3
    Hand-chosen threshold in Section 3.1; determines whether ViT blocks are skipped, directly affects the accuracy-efficiency trade-off.
  • Loss weights lambda1, lambda2 = 2, 2
    Set in Section 2.3; hand-chosen weights for the classification and SIoU losses.
assumptions (3)
  • standard math Cross-attention and ViT backbones work as described in the cited literature.
    Used in Section 2.1 without proof.
  • domain assumption Fusing static and dynamic template features via cross-attention improves nighttime feature robustness.
    The paper states this in Section 2.1, but it is only empirically tested, not derived.
  • domain assumption Skipping ViT layers based on the activation probability does not harm tracking accuracy.
    Core assumption of the DFA in Section 2.2; supported only by the ablation in Table 4 without theoretical justification.

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Cite this review

Pith. "Pith review of DARTer: Dynamic Adaptive Representation Tracker for Nighttime UAV Tracking." pith.science (2026). https://pith.science/paper/WMYA73F3

@misc{pith2026250500752,
  author       = {Pith},
  title        = {Pith review of: DARTer: Dynamic Adaptive Representation Tracker for Nighttime UAV Tracking},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WMYA73F3}},
  note         = {Machine review of arXiv:2505.00752}
}
read the original abstract

Nighttime UAV tracking presents significant challenges due to extreme illumination variations and viewpoint changes, which severely degrade tracking performance. Existing approaches either rely on light enhancers with high computational costs or introduce redundant domain adaptation mechanisms, failing to fully utilize the dynamic features in varying perspectives. To address these issues, we propose \textbf{DARTer} (\textbf{D}ynamic \textbf{A}daptive \textbf{R}epresentation \textbf{T}racker), an end-to-end tracking framework designed for nighttime UAV scenarios. DARTer leverages a Dynamic Feature Blender (DFB) to effectively fuse multi-perspective nighttime features from static and dynamic templates, enhancing representation robustness. Meanwhile, a Dynamic Feature Activator (DFA) adaptively activates Vision Transformer layers based on extracted features, significantly improving efficiency by reducing redundant computations. Our model eliminates the need for complex multi-task loss functions, enabling a streamlined training process. Extensive experiments on multiple nighttime UAV tracking benchmarks demonstrate the superiority of DARTer over state-of-the-art trackers. These results confirm that DARTer effectively balances tracking accuracy and efficiency, making it a promising solution for real-world nighttime UAV tracking applications.

Figures

Figures reproduced from arXiv: 2505.00752 by the authors.

Figure 1
Figure 1. (a) Overview architecture of DARTer. The nighttime dynamic features of the static and dynamic templates are fused. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Qualitative comparison results of our tracker with [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

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Works this paper leans on

49 extracted references · 31 canonical work pages

  1. [1]

    Abdulla Al-Kaff, María José Gómez-Silva, Francisco Miguel Moreno, Arturo De La Escalera, and José María Armingol. 2019. An appearance-based tracking algorithm for aerial search and rescue purposes. Sensors 19, 3 (2019), 652

    work page 2019

  2. [2]

    Dosovitskiy Alexey. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv: 2010.11929 (2020)

    arXiv 2020

  3. [3]

    Goutam Bhat, Martin Danelljan, Luc Van Gool, and Radu Timofte. 2019. Learning discriminative model prediction for tracking. In Proceedings of the IEEE/CVF international conference on computer vision . 6182–6191

    work page 2019

  4. [4]

    Ziang Cao, Changhong Fu, Junjie Ye, Bowen Li, and Yiming Li. 2021. HiFT: Hier- archical Feature Transformer for Aerial Tracking. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV) . 15457–15466

    work page 2021

  5. [5]

    Ziang Cao, Changhong Fu, Junjie Ye, Bowen Li, and Yiming Li. 2021. SiamAPN++: Siamese attentional aggregation network for real-time UAV tracking. In 2021 IEEE/RSJ international conference on intelligent robots and systems (IROS) . IEEE, 3086–3092

    work page 2021

  6. [6]

    Ziang Cao, Ziyuan Huang, Liang Pan, Shiwei Zhang, Ziwei Liu, and Changhong Fu. 2022. TCTrack: Temporal Contexts for Aerial Tracking. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022), 14778– 14788

    work page 2022

  7. [7]

    Ziang Cao, Ziyuan Huang, Liang Pan, Shiwei Zhang, Ziwei Liu, and Changhong Fu. 2023. Towards Real-World Visual Tracking with Temporal Contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023)

    work page 2023

  8. [8]

    Yutao Cui, Cheng Jiang, Limin Wang, and Gangshan Wu. 2022. Mixformer: End- to-end tracking with iterative mixed attention. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 13608–13618

    2022

Show all 49 references
  1. [9]

    Martin Danelljan, Luc Van Gool, and Radu Timofte. 2020. Probabilistic regression for visual tracking. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 7183–7192

    2020

  2. [10]

    Heng Fan, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, Hexin Bai, Yong Xu, Chunyuan Liao, and Haibin Ling. 2019. Lasot: A high-quality benchmark for large-scale single object tracking. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition ....

    2019

  3. [11]

    Xiaokun Feng, Xuchen Li, Shiyu Hu, Dailing Zhang, Meiqi Wu, Jing Zhang, Xiaotang Chen, and Kaiqi Huang. 2024. MemVLT: Vision-Language Tracking with Adaptive Memory-based Prompts. In The Thirty-eighth Annual Conference on Neural Information Processing Systems

    2024

  4. [12]

    Changhong Fu, Ziang Cao, Yiming Li, Junjie Ye, and Chen Feng. 2021. Siamese anchor proposal network for high-speed aerial tracking. In2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 510–516

    2021

  5. [13]

    Changhong Fu, Haolin Dong, Junjie Ye, Guangze Zheng, Sihang Li, and Jilin Zhao. 2022. HighlightNet: highlighting low-light potential features for real-time UAV tracking. In2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 12146–12153

    2022

  6. [14]

    Changhong Fu, Yiheng Wang, Liangliang Yao, Guangze Zheng, Haobo Zuo, and Jia Pan. 2024. Prompt-Driven Temporal Domain Adaptation for Nighttime UAV Tracking. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 9706–9713

    2024

  7. [15]

    Zhora Gevorgyan. 2022. SIoU loss: More powerful learning for bounding box regression. arXiv preprint arXiv:2205.12740 (2022)

    2022 arXiv

  8. [16]

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition . 770–778

    2016

  9. [17]

    Shiyu Hu, Dailing Zhang, Xiaokun Feng, Xuchen Li, Xin Zhao, Kaiqi Huang, et al

  10. [18]

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

    2019

  11. [19]

    Wang, Houwen Peng, and Huchuan Lu

    Ben Kang, Xin Chen, D. Wang, Houwen Peng, and Huchuan Lu. 2023. Exploring Lightweight Hierarchical Vision Transformers for Efficient Visual Tracking. 2023 IEEE/CVF International Conference on Computer Vision (ICCV) (2023), 9578–9587. https://api.semanticscholar.org/CorpusID:260887522

    2023

  12. [20]

    Bowen Li, Changhong Fu, Fangqiang Ding, Junjie Ye, and Fuling Lin. 2021. AD- Track: Target-aware dual filter learning for real-time anti-dark UAV tracking. In 2021 IEEE international conference on robotics and automation (ICRA) . IEEE, 496–502

    2021

  13. [21]

    Bowen Li, Changhong Fu, Fangqiang Ding, Junjie Ye, and Fuling Lin. 2022. All- day object tracking for unmanned aerial vehicle. IEEE Transactions on Mobile Computing 22, 8 (2022), 4515–4529

    2022

  14. [22]

    Bo Li, Wei Wu, Qiang Wang, Fangyi Zhang, Junliang Xing, and Junjie Yan. 2019. Siamrpn++: Evolution of siamese visual tracking with very deep networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 4282–4291

    2019

  15. [23]

    Bo Li, Junjie Yan, Wei Wu, Zheng Zhu, and Xiaolin Hu. 2018. High performance visual tracking with siamese region proposal network. In Proceedings of the IEEE conference on computer vision and pattern recognition . 8971–8980

    2018

  16. [24]

    Shuiwang Li, Yangxiang Yang, Dan Zeng, and Xucheng Wang. 2023. Adaptive and background-aware vision transformer for real-time uav tracking. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 13989–14000

    2023

  17. [25]

    Xuchen Li, Xiaokun Feng, Shiyu Hu, Meiqi Wu, Dailing Zhang, Jing Zhang, and Kaiqi Huang. 2024. 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 . 7283–7292

    2024

  18. [26]

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

    2024 arXiv

  19. [27]

    Xuchen Li, Shiyu Hu, Xiaokun Feng, Dailing Zhang, Meiqi Wu, Jing Zhang, and Kaiqi Huang. 2024. 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

  20. [28]

    Yongxin Li, Mengyuan Liu, You Wu, Xucheng Wang, Xiangyang Yang, and Shuiwang Li. 2024. Learning Adaptive and View-Invariant Vision Transformer for Real-Time UAV Tracking. InForty-first International Conference on Machine Learning

    2024

  21. [29]

    Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014. Microsoft coco: Common objects in context. In European Conference on Computer Vision (ECCV)

    2014

  22. [30]

    Yuen Peng Loh and Chee Seng Chan. 2019. Getting to know low-light images with the exclusively dark dataset. Computer Vision and Image Understanding 178 (2019), 30–42

    2019

  23. [31]

    I Loshchilov. 2017. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)

    2017 arXiv

  24. [32]

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

    2018

  25. [33]

    Li Shen, Xuyi Fan, and Hongguang Li. 2024. Overlapped Trajectory-Enhanced Visual Tracking. IEEE Transactions on Circuits and Systems for Video Technology (2024)

    2024

  26. [34]

    Tao Sun, Mattia Segu, Janis Postels, Yuxuan Wang, Luc Van Gool, Bernt Schiele, Federico Tombari, and Fisher Yu. 2022. SHIFT: a synthetic driving dataset for con- tinuous multi-task domain adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Reco...

    2022

  27. [35]

    Bin Tian, Qingming Yao, Yuan Gu, Kunfeng Wang, and Ye Li. 2011. Video process- ing techniques for traffic flow monitoring: A survey. In 2011 14th international IEEE conference on intelligent transportation systems (ITSC) . IEEE, 1103–1108

    2011

  28. [36]

    A Vaswani. 2017. Attention is all you need. Advances in Neural Information Processing Systems (2017)

    2017

  29. [37]

    Xing Wei, Yifan Bai, Yongchao Zheng, Dahu Shi, and Yihong Gong. 2023. Autore- gressive visual tracking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 9697–9706

    2023

  30. [38]

    You Wu, Xiangyang Yang, Xucheng Wang, Hengzhou Ye, Dan Zeng, and Shui- wang Li. 2024. MambaNUT: Nighttime UAV Tracking via Mamba and Adaptive Curriculum Learning. arXiv preprint arXiv:2412.00626 (2024)

    2024 arXiv

  31. [39]

    Liangliang Yao, Changhong Fu, and et al. 2023. SGDViT: Saliency-Guided Dy- namic Vision Transformer for UAV Tracking. arXiv preprint arXiv:2303.04378 (2023)

    2023 arXiv

  32. [40]

    Botao Ye, Hong Chang, Bingpeng Ma, Shiguang Shan, and Xilin Chen. 2022. Joint feature learning and relation modeling for tracking: A one-stream framework. In European Conference on Computer Vision . Springer, 341–357

    2022

  33. [41]

    Junjie Ye, Changhong Fu, Ziang Cao, Shan An, Guangze Zheng, and Bowen Li

  34. [42]

    Junjie Ye, Changhong Fu, Guangze Zheng, Danda Pani Paudel, and Guang Chen

  35. [43]

    Fisher Yu, Haofeng Chen, Xin Wang, Wenqi Xian, Yingying Chen, Fangchen Liu, Vashisht Madhavan, and Trevor Darrell. 2020. Bdd100k: A diverse driving dataset for heterogeneous multitask learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognitio...

    2020

  36. [44]

    Zhipeng Zhang, Houwen Peng, Jianlong Fu, Bing Li, and Weiming Hu. 2020. Ocean: Object-aware anchor-free tracking. In European Conference on Computer Vision (ECCV)

    2020

  37. [45]

    In Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Unsupervised domain adaptation for nighttime aerial tracking. In Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition . 8896–8905

  38. [46]

    Jiawen Zhu, Huayi Tang, Zhi-Qi Cheng, Jun-Yan He, Bin Luo, Shihao Qiu, Sheng- ming Li, and Huchuan Lu. 2024. Dcpt: Darkness clue-prompted tracking in nighttime uavs. In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 7381–7388

    2024

  39. [48]

    Haojie Zhao, Dong Wang, and Huchuan Lu. 2023. Representation Learning for Visual Object Tracking by Masked Appearance Transfer. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . 18696–18705

    2023

  40. [2022]

    IEEE Robotics and Automation Letters 7, 2 (2022), 3866–3873

    Tracker meets night: A transformer enhancer for UAV tracking. IEEE Robotics and Automation Letters 7, 2 (2022), 3866–3873

    2022

  41. [2024]

    Advances in Neural Information Processing Systems 36 (2024)

    A multi-modal global instance tracking benchmark (mgit): Better locating target in complex spatio-temporal and causal relationship. Advances in Neural Information Processing Systems 36 (2024)

    2024

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