SwinTrack: A Simple and Strong Baseline for Transformer Tracking
By Liting Lin, Heng Fan, Yong Xu, Haibin Ling. Neural Information Processing Systems, 2021
532 citations recorded by the source corpus.
Recently Transformer has been largely explored in tracking and shown state-of-the-art (SOTA) performance. However, existing efforts mainly focus on fusing and enhancing features generated by convolutional neural networks (CNNs). The potential of Transformer in representation learning remains under-explored. In this paper, we aim to further unleash the power of Transformer by proposing a simple yet efficient fully-attentional tracker, dubbed SwinTrack, within classic Siamese framework. In particular, both representation learning and feature fusion in SwinTrack leverage the Transformer architecture, enabling better feature interactions for tracking than pure CNN or hybrid CNN-Transformer frameworks. Besides, to further enhance robustness, we present a novel motion token that embeds historical target trajectory to improve tracking by providing temporal context. Our motion token is lightweight with negligible computation but brings clear gains. In our thorough experiments, SwinTrack exceeds existing approaches on multiple benchmarks. Particularly, on the challenging LaSOT, SwinTrack sets a new record with 0.713 SUC score. It also achieves SOTA results on other benchmarks. We expect SwinTrack to serve as a solid baseline for Transformer tracking and facilitate future research. Our codes and results are released at https://github.com/LitingLin/SwinTrack.
Subfields: Computer Networks and Communications, Control and Systems Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/91a4cbae6553e975ddc3b2f6850ed725ff475307
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In Computer Networks and Communications
In Electrical and Electronic Engineering
Citations in the corpus
- SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer (2022, background): “There are many Transformer-based models that have achieved impressive performance in various vision tasks, such as visual recognition [29], [63], [64], object detection [30], [65], [66], tracking [67]–[69], segmentation [31], [70], and image restoration [32], [33], [71].”
- Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework (2022, methodology): “While previous Transformer fusion trackers [5,29,47] random initialize the weights of Transformer layers, our joint feature learning and relation modeling module can directly benefit from the pre-trained weights.”
- SeqTrack: Sequence to Sequence Learning for Visual Object Tracking (2023, methodology): “SeqTrack-L384 outperforms the prior best tracker SwinTrack [29] by 1.2%.”