Papers › Transformer Tracking
Transformer Tracking
Xin Chen, Bin Yan, Jiawen Zhu, Dong Wang, Xiaoyun Yang, Huchuan Lu
Correlation acts as a critical role in the tracking field, especially in recent popular Siamese-based trackers. The correlation operation is a simple fusion manner to consider the similarity between the template and the search region. However, the correlation operation itself is a local linear matching process, leading to lose semantic information and fall into local optimum easily, which may be the bottleneck of designing high-accuracy tracking algorithms. Is there any better feature fusion method than correlation? To address this issue, inspired by Transformer, this work presents a novel attention-based feature fusion network, which effectively combines the template and search region features solely using attention. Specifically, the proposed method includes an ego-context augment module based on self-attention and a cross-feature augment module based on cross-attention. Finally, we present a Transformer tracking (named TransT) method based on the Siamese-like feature extraction backbone, the designed attention-based fusion mechanism, and the classification and regression head. Experiments show that our TransT achieves very promising results on six challenging datasets, especially on large-scale LaSOT, TrackingNet, and GOT-10k benchmarks. Our tracker runs at approximatively 50 fps on GPU. Code and models are available at https://github.com/chenxin-dlut/TransT.
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Tasks
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Object Tracking | COESOT | TransT | Precision Rate | 67.9 | #6 of 12 | Archive leaderboard | report |
| Object Tracking | COESOT | TransT | Success Rate | 60.5 | #6 of 12 | Archive leaderboard | report |
| Video Object Tracking | NT-VOT211 | TransT | AUC | 36.79 | #13 of 43 | Archive leaderboard | report |
| Video Object Tracking | NT-VOT211 | TransT | Precision | 51.97 | #13 of 43 | Archive leaderboard | report |
| Visual Object Tracking | AVisT | TransT | Success Rate | 49.03 | #7 of 7 | Archive leaderboard | report |
| Visual Object Tracking | DiDi | TransT | Tracking quality | 0.465 | #11 of 11 | Archive leaderboard | report |
| Visual Object Tracking | LaSOT | TransT | AUC | 64.9 | #37 of 46 | Archive leaderboard | report |
| Visual Object Tracking | LaSOT | TransT | Normalized Precision | 73.8 | #37 of 46 | Archive leaderboard | report |
| Visual Object Tracking | LaSOT | TransT | Precision | 69.0 | #37 of 46 | Archive leaderboard | report |
| Visual Tracking | TNL2K | TransT | AUC | 50.7 | #5 of 6 | 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.
Methods
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