TL;DR
This paper introduces TransT, a novel Transformer-based tracking method that uses attention mechanisms for feature fusion, outperforming correlation-based methods and achieving high accuracy and speed on multiple benchmarks.
Contribution
The work proposes an attention-based feature fusion network for tracking, replacing correlation, and demonstrates its effectiveness with state-of-the-art results.
Findings
Achieves promising results on six challenging datasets.
Runs at approximately 50 fps on GPU.
Outperforms correlation-based trackers on large-scale benchmarks.
Abstract
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…
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Taxonomy
MethodsLinear Layer · Absolute Position Encodings · Position-Wise Feed-Forward Layer · Adam · Softmax · Dense Connections · Attention Is All You Need · Dropout · Layer Normalization · Residual Connection
