Transforming Model Prediction for Tracking Christoph Mayer, Martin Danelljan, Goutam Bhat, Matthieu Paul, Danda Pani Paudel, Fisher Yu, Luc Van Gool Transforming Model Prediction for Tracking Optimization based tracking methods have been widely successful by integrating a target model prediction module, providing effective global reasoning by minimizing an objective function. While this inductiv..

Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework Botao Ye 1 , 2 , Hong Chang 1 , 2 , Bingpeng Ma 2 , Shiguang Shan 1 , 2 , and Xilin Chen 1 , 2 Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework The current popular two-stream, two-stage tracking framework extracts the template and the search region features separately and then perfo..

HiFT : Hierarchical Feature Transformer for Aerial Tracking Ziang Cao† , Changhong Fu†,*, Junjie Ye† , Bowen Li† , and Yiming Li‡ HiFT: Hierarchical Feature Transformer for Aerial Tracking Most existing Siamese-based tracking methods execute the classification and regression of the target object based on the similarity maps. However, they either employ a single map from the last convolutional la..

AiATrack : Attention in Attention for Transformer Visual Tracking Shenyuan Gao1 , Chunluan Zhou2 , Chao Ma3 , Xinggang Wang1 , Junsong Yuan4 AiATrack: Attention in Attention for Transformer Visual Tracking Transformer trackers have achieved impressive advancements recently, where the attention mechanism plays an important role. However, the independent correlation computation in the attention me..

SwinTrack: A Simple and Strong Baseline for Transformer Tracking Liting Lin1,2∗ Heng Fan3∗ Zhipeng Zhang4 Yong Xu1,2 Haibin Ling5 SwinTrack: A Simple and Strong Baseline for Transformer Tracking 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 convolutiona..

Target Transformed Regression for Accurate Tracking Yutao Cui Cheng Jiang Limin Wang* Gangshan Wu 0. Abstract 비디오에서 target의 (appearance variations, pose, view changes, geometric deformations)으로 인해 정확한 추적은 여전히 어려운 작업이다. 최근의 anchor-free trackers는 효율적인 regression mechanism을 제공하지만, 정확한 bounding box 추정은 하지 못한다. 이러한 문제를 해결하기 위해 본 논문은 정확한 anchor-free 추적을 위해 TREG라고 하는 Transformer-alike regression branch..

SparseTT: Visual Tracking with Sparse Transformers Zhihong Fu , Zehua Fu , Qingjie Liu∗ , Wenrui Cai and Yunhong Wang SparseTT: Visual Tracking with Sparse Transformers Transformers have been successfully applied to the visual tracking task and significantly promote tracking performance. The self-attention mechanism designed to model long-range dependencies is the key to the success of Transform..