Papers › Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking

Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking

22 Mar 2021CVPR 2021 1arXiv:2103.11681archive 2025-07-28

Ning Wang, Wengang Zhou, Jie Wang, Houqaing Li

In video object tracking, there exist rich temporal contexts among successive frames, which have been largely overlooked in existing trackers. In this work, we bridge the individual video frames and explore the temporal contexts across them via a transformer architecture for robust object tracking. Different from classic usage of the transformer in natural language processing tasks, we separate its encoder and decoder into two parallel branches and carefully design them within the Siamese-like tracking pipelines. The transformer encoder promotes the target templates via attention-based feature reinforcement, which benefits the high-quality tracking model generation. The transformer decoder propagates the tracking cues from previous templates to the current frame, which facilitates the object searching process. Our transformer-assisted tracking framework is neat and trained in an end-to-end manner. With the proposed transformer, a simple Siamese matching approach is able to outperform the current top-performing trackers. By combining our transformer with the recent discriminative tracking pipeline, our method sets several new state-of-the-art records on prevalent tracking benchmarks.

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InstanceL2Norm 594422814/TransformerTrack/ltr/models/target_classifier/transformer.py official repository ran · metamorphic tier: deterministic fingerprinted MIT (permissive) · c39d16d0426c394c · report
MultiheadAttention 594422814/TransformerTrack/ltr/models/target_classifier/transformer.py official repository ran · metamorphic tier: deterministic MIT (permissive) · 1b733e1fccc5d345 · report
TransformerDecoderLayer 594422814/TransformerTrack/ltr/models/target_classifier/transformer.py official repository ran · metamorphic tier: deterministic MIT (permissive) · 81552d44911a524a · report
TransformerEncoder 594422814/TransformerTrack/ltr/models/target_classifier/transformer.py official repository ran · metamorphic tier: deterministic MIT (permissive) · f9081530a58aa58c · report
TransformerEncoderLayer 594422814/TransformerTrack/ltr/models/target_classifier/transformer.py official repository ran · metamorphic tier: deterministic MIT (permissive) · 81d1975ee00caff6 · report
_get_clones 594422814/TransformerTrack/ltr/models/target_classifier/transformer.py official repository ran · our draft was wrong MIT (permissive) · 18c99336939119a2 · report
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TransformerDecoder 594422814/TransformerTrack/ltr/models/target_classifier/transformer.py official repository unverified MIT (permissive) · b5c213461ba61b6f · report

Tasks

DecoderObjectObject TrackingVideo Object TrackingVisual Object TrackingVisual Tracking

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Object Tracking COESOT TrDiMP Precision Rate 66.9 #8 of 12 Archive leaderboard report
Object Tracking COESOT TrDiMP Success Rate 60.1 #8 of 12 Archive leaderboard report
Video Object Tracking NT-VOT211 TrDiMP AUC 36.66 #14 of 43 Archive leaderboard report
Video Object Tracking NT-VOT211 TrDiMP Precision 50.68 #14 of 43 Archive leaderboard report
Visual Object Tracking LaSOT TrDiMP AUC 63.7 #39 of 46 Archive leaderboard report
Visual Object Tracking LaSOT TrDiMP Precision 61.4 #39 of 46 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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