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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2022.3232535
|2 doi
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|a pubmed24n1183.xml
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|a (DE-627)NLM355202557
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|a (NLM)37015509
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Chen, Xin
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a High-Performance Transformer Tracking
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| 264 |
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|c 2023
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 06.06.2023
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| 500 |
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|a Date Revised 06.06.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Correlation has a critical role in the tracking field, especially in recent popular Siamese-based trackers. The correlation operation is a simple fusion method that considers the similarity between the template and the search region. However, the correlation operation is a local linear matching process, losing semantic information and easily falling into a local optimum, which may be the bottleneck in designing high-accuracy tracking algorithms. In this work, to determine whether a better feature fusion method exists than correlation, a novel attention-based feature fusion network, inspired by the transformer, is presented. This network effectively combines the template and search region features using attention mechanism. Specifically, the proposed method includes an ego-context augment module based on self-attention and a cross-feature augment module based on cross-attention. First, 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 heads. Based on the TransT baseline, we also design a segmentation branch to generate the accurate mask. Finally, we propose a stronger version of TransT by extending it with a multi-template scheme and an IoU prediction head, named TransT-M. Experiments show that our TransT and TransT-M methods achieve promising results on seven popular benchmarks. Code and models are available at https://github.com/chenxin-dlut/TransT-M
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| 650 |
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4 |
|a Journal Article
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| 700 |
1 |
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|a Yan, Bin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhu, Jiawen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Lu, Huchuan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ruan, Xiang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Dong
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 7 vom: 27. Juli, Seite 8507-8523
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
1 |
8 |
|g volume:45
|g year:2023
|g number:7
|g day:27
|g month:07
|g pages:8507-8523
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| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TPAMI.2022.3232535
|3 Volltext
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