Papers › Transformer Tracking

Transformer Tracking

29 Mar 2021CVPR 2021 1arXiv:2103.15436archive 2025-07-28

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

Object TrackingVideo Object TrackingVisual Object TrackingVisual Tracking

1 archive task tag without a task page not shown.

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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