20 citations · 28 across the 5 of their papers we have counts for
6 papers
Unsupervised Learning of Accurate Siamese Tracking
Qiuhong Shen, Lei Qiao, Jinyang Guo +7
Unsupervised learning has been popular in various computer vision tasks, including visual object tracking. However, prior unsupervised tracking approaches rely heavily on spatial s…
GLiT: Neural Architecture Search for Global and Local Image Transformer
Boyu Chen, Peixia Li, Chuming Li +6
We introduce the first Neural Architecture Search (NAS) method to find a better transformer architecture for image recognition. Recently, transformers without CNN-based backbones a…
BN-NAS: Neural Architecture Search with Batch Normalization
Boyu Chen, Peixia Li, Baopu Li +5
We present BN-NAS, neural architecture search with Batch Normalization (BN-NAS), to accelerate neural architecture search (NAS). BN-NAS can significantly reduce the time required b…
PSViT: Better Vision Transformer via Token Pooling and Attention Sharing
Boyu Chen, Peixia Li, Baopu Li +6
In this paper, we observe two levels of redundancies when applying vision transformers (ViT) for image recognition. First, fixing the number of tokens through the whole network pro…
Real-Time Visual Object Tracking via Few-Shot Learning
Jinghao Zhou, Bo Li, Peng Wang +5
Visual Object Tracking (VOT) can be seen as an extended task of Few-Shot Learning (FSL). While the concept of FSL is not new in tracking and has been previously applied by prior wo…
GradNet: Gradient-Guided Network for Visual Object Tracking
Peixia Li, Boyu Chen, Wanli Ouyang +3
The fully-convolutional siamese network based on template matching has shown great potentials in visual tracking. During testing, the template is fixed with the initial target feat…