FocusTrack: A Self-Adaptive Local Sampling Algorithm for Efficient Anti-UAV Tracking

Jianan Li; Tingfa Xu; Ying Wang

REVIEW 3 major objections 5 minor 41 references

FocusTrack: A Self-Adaptive Local Sampling Algorithm for Efficient Anti-UAV Tracking

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read FocusTrack claims that a local tracker with a self-adaptive search region and mask-refined features matches global trackers on anti-UAV benchmarks while costing a fraction of the compute.

desk verdict Solid, reproducible engineering on a hard benchmark; the adaptive search mechanism is real but its presence classifier is trained on the wrong contrast, and the headline numbers lean on test-set-tuned thresholds. read the letter →

arxiv 2504.13604 v1 pith:CVCTVFZ3 submitted 2025-04-18 cs.CV

Ying Wang , Tingfa Xu , Jianan Li This is my paper · ORCID

classification cs.CV

keywords anti-UAVtrackingsingleobjectthermalinfraredvisiontransformeradaptivesearchregionattention-to-masksmalltarget

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

FocusTrack tries to settle a trade-off in anti-UAV tracking: local trackers are fast but lose small drones when the camera jerks, while global trackers that scan the whole frame rarely lose the target but are too slow for real-time use. The paper argues that a local tracker can get most of the global tracker's robustness by making its search-region size responsive to an estimated probability that the target is still in view, and by using a mask-based attention module to keep the target representation sharp when the field of view widens. On the thermal-infrared AntiUAV and AntiUAV410 benchmarks, FocusTrack reports 67.7% and 62.8% AUC, 8.5 and 9.1 points above its OSTrack baseline. If the result holds, a tracker that costs only 30G MACs and runs at 44-143 fps can replace global re-detection trackers in drone-defense applications.

What carries the argument

The load-bearing mechanism is the pair (SRA, ATM). SRA is an adaptive search-region controller: a learnable CLS token is prepended to the concatenated template/search tokens of the ViT backbone, trained with cross-entropy on positive pairs from the same video and negative pairs from different videos, and at inference its logits, combined with the peak score of the classification map, decide whether the search factor grows by a step of 1 up to a maximum of 8 (thresholds $T_{logits}=0.8$ and $T_{score}=0.5$). ATM is a stack of three cross-attention blocks that convert a learnable query into sigmoid-activated masks from backbone layers 6, 8 and 12; the fused mask re-weights the classification map and is supervised by focal loss on rectangular pseudo-masks derived from bounding boxes. Together they keep the target inside the field of view while preventing a larger view from diluting target features.

What would settle it

Run FocusTrack on a held-out set of unseen anti-UAV sequences with extreme camera shakes or adversarially selected jumps that place the target outside a factor-8 window, and record whether the presence logits stay below 0.8 while the search factor climbs to its maximum without reacquiring the target. A concrete failure would be long runs where the classifier keeps the window small while the target is outside it, or where expansion reaches $f_{max}=8$ and the target is never recovered.

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Extended reading notes

Core claim

FocusTrack's central claim is that search-region size, not feature strength alone, is the main failure point for local trackers on anti-UAV videos, and that it can be made adaptive without paying a global-search cost. Its Search Region Adjustment (SRA) module prepends a CLS token to the template and search tokens of a ViT-based tracker, trains that token with contrastive positive/negative frame pairs, and then uses its presence logits plus the peak classification score to decide whether to keep the search factor at 6 or expand it stepwise up to 8. The Attention-to-Mask (ATM) module counteracts the feature dilution caused by larger fields of view by generating hierarchical segmentation-mask attention that sharpens the classification map. With both modules, the paper reports state-of-the-art success rates on AntiUAV (67.7% AUC) and AntiUAV410 (62.8% AUC), surpassing local baselines and coming within 4.0 AUC of the global tracker SiamDT while using roughly one-eighth of its compute.

Load-bearing premise

Everything rests on the presence classifier and its two fixed thresholds: if the CLS token fails to notice that the target has left the view, or the chosen thresholds do not transfer to new motion patterns, the search region never expands and the tracker has no way to recover the target.

Editorial extensions

If this is right

If FocusTrack's reported gains hold, local trackers can close most of the accuracy gap to global re-detection trackers on anti-UAV benchmarks at a fraction of the compute (30G MACs versus 225.3G for SiamDT).
The ablation shows that raising the fixed search factor from 4 to 6 adds 6.1 AUC and the adaptive SRA adds another 2.5 AUC, meaning search-window sizing is a first-order error source for small-target tracking, not a minor tuning detail.
The full FocusTrack stays real-time at 44 fps while also producing segmentation masks; dropping the ATM module still yields 62.3% AUC at 143 fps, so the adaptive window alone carries most of the benefit.

Reading between the lines

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

My inference: the fixed thresholds $T_{logits}=0.8$ and $T_{score}=0.5$ were tuned on test-set sweeps, so a real deployment would need online calibration because a motion pattern that fools the presence classifier will exhaust the $f_{max}=8$ expansion and permanently lose the target.
My inference: because the presence classifier is trained on pairs drawn from the same benchmark, it may be sensitive to domain shift in camera motion; a testable extension is to train SRA on motion-perturbed frames with pseudo-presence labels.
My inference: ATM's mask output opens a path toward joint tracking and segmentation-based small-target detection, and replacing rectangular pseudo-masks with finer annotations could improve performance on tiny-detail cases.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Short version: this is a legitimate, reasonably careful engineering paper that deserves proper peer review, but not a clean accept as-is. The new thing is the SRA scheduler—confidence-triggered search-factor expansion for a local transformer tracker—plus the ATM mask refinement ported from SegViT. The gains over a fixed search factor are real: simply widening OSTrack's search from 4 to 6 gives 6.1 AUC on AntiUAV410, and SRA adds another 2.1. That is a meaningful efficiency-accuracy trade for anti-UAV tracking.

Credit where due: they retrain all competing local trackers on the same training set, report MACs and fps on the same GPU, and release code. The ablations are internally consistent, and the paper honestly shows that the largest single gain comes from a larger fixed search region, not from the adaptive mechanism.

Soft spots, in order of seriousness. First, the SRA presence classifier is trained with negative pairs from different videos, so the contrastive supervision encodes sequence identity, not target absence in a same-video crop. The stress-test raised this, and I think it lands. The Pmax threshold partially papers over it because the classification peak also gates expansion, but the paper never analyzes whether the CLS logits actually drop when the target leaves a same-video crop. The qualitative Fig. 10 cannot distinguish the two cues. Second, all four inference thresholds (Tlogits, Tscore, fmax, fstep) are selected by sweeping on the test set (Table VI). That makes the reported degree of improvement partly a selection artifact; there is no validation split and no error bars. Third, the "state-of-the-art" claim is overbroad: SiamDT gets 66.8 AUC on AntiUAV410, 4 points higher, at 8 fps. FocusTrack's point is the efficiency-accuracy trade, not absolute SOTA.

None of this is fatal. The core claim—a local tracker with adaptive search can get within 4 AUC of a global tracker at 5x speed—holds up as an engineering result. The paper would be stronger if they retrained SRA with same-video target-absent negatives, fixed thresholds on validation, and reported standard errors. Who is it for? People working on anti-UAV or long-term tracking with abrupt motion. I would cite it. It deserves refereeing—the claims are concrete, artifacts are available, and the core idea is worth scrutiny. I would accept it conditionally on addressing the SRA training mismatch and moving threshold selection off the test set.

Referee Report

3 major / 5 minor

Summary. The paper proposes FocusTrack, a local anti-UAV tracker built on OSTrack, adding two modules: Search Region Adjustment (SRA), which uses a CLS token to estimate a target-presence probability and adaptively expands the search crop during inference when confidence is low; and Attention-to-Mask (ATM), which aggregates search features from multiple backbone layers via cross-attention to produce a mask that refines the classification score map. The method is trained on the AntiUAV410 training set and evaluated on both AntiUAV and AntiUAV410, reporting 67.7% and 62.8% AUC, respectively, and outperforming retrained local baselines by 8.5-9.1 AUC points. The paper also reports 30G MACs, 143 fps for the SRA-only variant, and 44 fps for the full version, and includes extensive ablations and attribute-based analyses.

Significance. If the reported numbers hold, FocusTrack offers a credible efficiency-accuracy trade-off for anti-UAV tracking: the SRA-only variant is an order of magnitude cheaper than global redetection trackers, and the full version improves over strong local baselines by a substantial margin. The retraining of all local baselines on the same training set is a fairer comparison than relying on published numbers, and the module ablations in Tables IV-VIII are internally consistent. However, the headline 'state-of-the-art' claim is not supported by Table III, where the global tracker SiamDT reaches 66.8% AUC versus FocusTrack's 62.8% AUC, and the core generalization claims rest on two methodological choices that need scrutiny: the contrastive training signal for the presence classifier and the selection of inference thresholds on the test set.

major comments (3)

[III-B2] The negative pairs for the SRA presence classifier are defined as frames from different sequences, while the inference-time failure the classifier is meant to detect is a target-absent search crop from the same sequence (e.g., abrupt camera motion or out-of-view events). A cross-video negative pair can be solved by global appearance or sequence-identity cues, so the CLS logits are not necessarily evidence that the target is absent from the current search crop. The qualitative visualization in Fig. 10 cannot separate these cues. This is load-bearing because Algorithm 1 expands the search region only when both logits<T_logits and Pmax<T_score. Please add same-video negative pairs in which the target is outside the search crop, and report an ablation or per-sequence logits around out-of-view events showing that logits drop specifically for same-video target absence.
[IV-A4, Table VI] The four inference hyperparameters Tlogits=0.8, Tscore=0.5, fmax=8, and fstep=1 are selected by sweeping on the AntiUAV410 test set in Table VI, and no separate validation split or repeated runs are reported. The final 62.8% AUC is therefore in part a test-set-selected number, and the 0.2-point difference between the best Table VI configuration (62.6%) and the final two-phase result (62.8%) is within the range that could be noise. Please tune the thresholds on the 90-sequence validation split or via cross-validation, report test numbers for the validation-tuned configuration, and provide error bars over multiple runs.
[Abstract, Conclusion, Table III] The paper claims 'state-of-the-art performance,' but Table III reports SiamDT at 66.8% AUC and 90.0% precision on AntiUAV410 versus FocusTrack's 62.8% AUC and 86.2% precision. The correct claim is that FocusTrack is state-of-the-art among local trackers and offers a much better efficiency-accuracy trade-off than the global tracker. Please revise the abstract and conclusion to avoid an unsupported global-state-of-the-art claim.

minor comments (5)

[Table III] The table header contains a typo: 'SPEES' should be 'SPEED'.
[IV-B2] The sentence 'the lower section presents results from models retrained on AntiUAV410 test set' should read 'training set', since the lower part of Table II is explicitly reported as retrained on the AntiUAV410 training set.
[Fig. 1 caption] The caption contains a typo: 'traget motion' should be 'target motion'.
[III-C] The section heading 'Attention-to Mask Module' should be hyphenated as 'Attention-to-Mask Module' for consistency with the text.
[IV-A3] In the Single-Phase Training description, the text says '60,000 positive samples per iteration' and then derives 85,715 total samples; please clarify whether these are per-iteration samples or per-epoch samples, and define the epoch/iteration relationship.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: FocusTrack's reported gains are empirical benchmark results, not analytic derivations from fitted inputs.

full rationale

FocusTrack is an empirical system paper with no analytic derivation that reduces to its inputs. The central accuracy claims (67.7% and 62.8% AUC) are measured on AntiUAV and AntiUAV410 test annotations, which are independent of the method's fitted constants. The SRA module is a learned classifier plus a hand-designed update rule (Algorithm 1); its thresholds are selected on the test set in Table VI, which is a selection-bias/evaluation-integrity concern rather than a circular prediction. The contrastive sampling in Section III-B2 labels same-video pairs as presence=1 and cross-video pairs as presence=0, which may leave a training/inference gap for same-video target-absent crops, but this is a generalization risk, not an identity between the training objective and the reported result. References [1], [2], and [16] are self-citations from the same group, but they serve as benchmarks/baselines and do not carry the derivational weight of FocusTrack's design. No equation in the paper is equivalent to another by construction, and no fitted parameter is renamed as a prediction. The ATM module is a direct adaptation of SegViT's cross-attention masks. Thus the paper is not circular; the flagged issues belong to robustness and test-set hygiene, not circularity.

Assumptions & free parameters 7 free parameters · 4 assumptions · 0 invented entities

No new physical or latent entities are introduced. The CLS token is a standard BERT/ViT mechanism and the ATM module is an existing segmentation architecture. The free parameters are inference-time thresholds and architecture choices, several of which were tuned directly on the test set.

free parameters (7)

Base search factor f_base = 6
Set from the empirical sweep in Fig. 2, where factor 6 beats 4 and 7 on AntiUAV410.
Search factor step f_step = 1
Selected by ablation on the AntiUAV410 test set (Table VI-d).
Maximum search factor f_max = 8
Selected by ablation on the AntiUAV410 test set (Table VI-c).
Logits threshold T_logits = 0.8
Selected by ablation on the AntiUAV410 test set (Table VI-a).
Score threshold T_score = 0.5
Selected by ablation on the AntiUAV410 test set (Table VI-b).
ATM input backbone layers = [6, 8, 12]
Selected by ablation on the AntiUAV410 test set (Table VII).
Number of ATM blocks = 3
Configuration inherited from SegViT and validated in Table VII.

assumptions (4)

domain assumption The CLS-token presence probability learned from same-sequence positive pairs and cross-sequence negative pairs generalizes to test-time target absence caused by abrupt camera motion and out-of-view events.
Section III-B2 defines the contrastive sampling; the paper provides no analysis of domain shift between training negatives and real camera-motion losses.
domain assumption The maximum search factor fmax=8 bounds every recoverable displacement; targets displaced beyond the expanded field cannot be reacquired.
Algorithm 1 caps the search factor at fmax, and the paper never quantifies the maximum inter-frame displacement covered by an 8x crop.
domain assumption Rectangular pseudo ground-truth masks generated from bounding boxes are adequate supervision for the ATM mask module.
Section IV-A2 states masks are generated from boxes as an approximation for small UAVs; this is not validated against true segmentation.
domain assumption The AntiUAV410 train/test annotations and evaluation protocol are correct, and the re-trained baselines are compared fairly.
All conclusions rest on benchmark ground truth; the authors include creators of the benchmark, which is a self-citation context but not a mathematical axiom.

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Cite this review

Pith. "Pith review of FocusTrack: A Self-Adaptive Local Sampling Algorithm for Efficient Anti-UAV Tracking." pith.science (2026). https://pith.science/paper/CVCTVFZ3

@misc{pith2026250413604,
  author       = {Pith},
  title        = {Pith review of: FocusTrack: A Self-Adaptive Local Sampling Algorithm for Efficient Anti-UAV Tracking},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CVCTVFZ3}},
  note         = {Machine review of arXiv:2504.13604}
}

read the original abstract

Anti-UAV tracking poses significant challenges, including small target sizes, abrupt camera motion, and cluttered infrared backgrounds. Existing tracking paradigms can be broadly categorized into global- and local-based methods. Global-based trackers, such as SiamDT, achieve high accuracy by scanning the entire field of view but suffer from excessive computational overhead, limiting real-world deployment. In contrast, local-based methods, including OSTrack and ROMTrack, efficiently restrict the search region but struggle when targets undergo significant displacements due to abrupt camera motion. Through preliminary experiments, it is evident that a local tracker, when paired with adaptive search region adjustment, can significantly enhance tracking accuracy, narrowing the gap between local and global trackers. To address this challenge, we propose FocusTrack, a novel framework that dynamically refines the search region and strengthens feature representations, achieving an optimal balance between computational efficiency and tracking accuracy. Specifically, our Search Region Adjustment (SRA) strategy estimates the target presence probability and adaptively adjusts the field of view, ensuring the target remains within focus. Furthermore, to counteract feature degradation caused by varying search regions, the Attention-to-Mask (ATM) module is proposed. This module integrates hierarchical information, enriching the target representations with fine-grained details. Experimental results demonstrate that FocusTrack achieves state-of-the-art performance, obtaining 67.7% AUC on AntiUAV and 62.8% AUC on AntiUAV410, outperforming the baseline tracker by 8.5% and 9.1% AUC, respectively. In terms of efficiency, FocusTrack surpasses global-based trackers, requiring only 30G MACs and achieving 143 fps with FocusTrack (SRA) and 44 fps with the full version, both enabling real-time tracking.

Figures

Figures reproduced from arXiv: 2504.13604 by the authors.

**Figure 2.** Preliminary experiments on OSTrack [3] to explore the impact [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** (a) Overall structure of FocusTrack. (b) Detailed explanation of the Adaptive Search Region Adjustment Strategy. FocusTrack takes the template, [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Figures from the paper (10 more)

**Figure 4.** Figure 4: Detailed explanation of the Contrastive Frame Sampling Strategy. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 5.** Figure 5: (a) Overall structure of ATM Module. (b) Detailed structure of a [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]

**Figure 6.** Figure 6: Success, Precision and Normalized Precision plots on AntiUAV410 test set. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

**Figure 7.** Figure 7: Attribute-based success performance analysis on the AntiUAV410 test set. Success plots are shown for six challenging scenarios: Dynamic Background [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]

**Figure 8.** Figure 8: Attribute-based evaluation on the AntiUAV410 test set. AUC score is [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]

**Figure 9.** Figure 9: Attribute-based precision performance analysis on the AntiUAV410 test set. Precision plots are shown for six challenging scenarios: Dynamic [PITH_FULL_IMAGE:figures/full_fig_p010_9.png]

**Figure 10.** Figure 10: Qualitative analysis of SRA strategy’s response to target position changes. The visualization shows target trajectories ( [PITH_FULL_IMAGE:figures/full_fig_p011_10.png]

**Figure 11.** Figure 11: Qualitative analysis of the ATM module’s segmentation performance [PITH_FULL_IMAGE:figures/full_fig_p011_11.png]

**Figure 12.** Figure 12: Qualitative visualization of four trackers under three challenging scenarios: FM (Fast Motion), SV (Scale Variation) and OC (Occlusion). Each row in [PITH_FULL_IMAGE:figures/full_fig_p012_12.png]

**Figure 13.** Figure 13: Qualitative visualization of four trackers under three challenging scenarios: TC (Thermal Crossover), OV (Out-of-View) and DBC (Dynamic [PITH_FULL_IMAGE:figures/full_fig_p012_13.png]

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M. Zhang, Q. Zhang, W. Song, D. Huang, and Q. He, “Promptvt: Prompting for efficient and accurate visual tracking,” IEEE Transactions on Circuits and Systems for Video Technology , 2024

2024
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Efficient visual tracking with exemplar transformers,

P. Blatter, M. Kanakis, M. Danelljan, and L. Van Gool, “Efficient visual tracking with exemplar transformers,” in Proceedings of the IEEE/CVF winter conference on applications of computer vision , 2023, pp. 1571– 1581

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Mixformerv2: Efficient fully transformer tracking,

Y . Cui, T. Song, G. Wu, and L. Wang, “Mixformerv2: Efficient fully transformer tracking,” Advances in Neural Information Processing Sys- tems, vol. 36, 2024

2024
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Generalized relation modeling for transformer tracking,

S. Gao, C. Zhou, and J. Zhang, “Generalized relation modeling for transformer tracking,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 18 686–18 695

2023
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Joint visual grounding and tracking with natural language specification,

L. Zhou, Z. Zhou, K. Mao, and Z. He, “Joint visual grounding and tracking with natural language specification,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2023, pp. 23 151–23 160

2023
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Seqtrack: Sequence to sequence learning for visual object tracking,

X. Chen, H. Peng, D. Wang, H. Lu, and H. Hu, “Seqtrack: Sequence to sequence learning for visual object tracking,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2023, pp. 14 572–14 581

2023
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Learning spatio-temporal transformer for visual tracking,

B. Yan, H. Peng, J. Fu, D. Wang, and H. Lu, “Learning spatio-temporal transformer for visual tracking,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 10 448–10 457

2021
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Tctrack: Temporal contexts for aerial tracking,

Z. Cao, Z. Huang, L. Pan, S. Zhang, Z. Liu, and C. Fu, “Tctrack: Temporal contexts for aerial tracking,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 14 798–14 808

2022
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Transforming model prediction for tracking,

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Aiatrack: Attention in attention for transformer visual tracking,

S. Gao, C. Zhou, C. Ma, X. Wang, and J. Yuan, “Aiatrack: Attention in attention for transformer visual tracking,” in European Conference on Computer Vision. Springer, 2022, pp. 146–164

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St- trans: Spatial-temporal transformer for infrared small target detection in sequential images,

X. Tong, Z. Zuo, S. Su, J. Wei, X. Sun, P. Wu, and Z. Zhao, “St- trans: Spatial-temporal transformer for infrared small target detection in sequential images,” IEEE Transactions on Geoscience and Remote Sensing, 2024

2024

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.

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Anti-uav410: A thermal infrared benchmark and customized scheme for tracking drones in the wild,

B. Huang, J. Li, J. Chen, G. Wang, J. Zhao, and T. Xu, “Anti-uav410: A thermal infrared benchmark and customized scheme for tracking drones in the wild,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023

work page 2023

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Siamsta: Spatio-temporal attention based siamese tracker for tracking uavs,

B. Huang, J. Chen, T. Xu, Y . Wang, S. Jiang, Y . Wang, L. Wang, and J. Li, “Siamsta: Spatio-temporal attention based siamese tracker for tracking uavs,” inProceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 1204–1212

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

B. Ye, H. Chang, B. Ma, S. Shan, and X. Chen, “Joint feature learning and relation modeling for tracking: A one-stream framework,” in European Conference on Computer Vision. Springer, 2022, pp. 341– 357

2022

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Robust object modeling for visual tracking,

Y . Cai, J. Liu, J. Tang, and G. Wu, “Robust object modeling for visual tracking,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 9589–9600

work page 2023

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Anti-uav: a large-scale benchmark for vision-based uav tracking,

N. Jiang, K. Wang, X. Peng, X. Yu, Q. Wang, J. Xing, G. Li, G. Guo, Q. Ye, J. Jiao et al., “Anti-uav: a large-scale benchmark for vision-based uav tracking,” IEEE Transactions on Multimedia , vol. 25, pp. 486–500, 2021

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Smalltrack: Wavelet pooling and graph enhanced classification for uav small object tracking,

Y . Xue, G. Jin, T. Shen, L. Tan, N. Wang, J. Gao, and L. Wang, “Smalltrack: Wavelet pooling and graph enhanced classification for uav small object tracking,” IEEE Transactions on Geoscience and Remote Sensing, 2023

work page 2023

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Efficient template distinction modeling tracker with temporal contexts for aerial tracking,

H. Zhang, W. Xing, H. Lin, H. Liu, and Y . Yang, “Efficient template distinction modeling tracker with temporal contexts for aerial tracking,” IEEE Transactions on Geoscience and Remote Sensing , 2024

work page 2024

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Mixformer: End-to-end tracking with iterative mixed attention,

Y . Cui, C. Jiang, L. Wang, and G. Wu, “Mixformer: End-to-end tracking with iterative mixed attention,” in Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition , 2022, pp. 13 608– 13 618

work page 2022