1.6k citations · 3.8k across the 68 of their papers we have counts for
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Diversity Transfer Network for Few-Shot Learning
Mengting Chen, Yuxin Fang, Xinggang Wang +6
Few-shot learning is a challenging task that aims at training a classifier for unseen classes with only a few training examples. The main difficulty of few-shot learning lies in th…
All You Need Is Boundary: Toward Arbitrary-Shaped Text Spotting
Hao Wang, Pu Lu, Hui Zhang +6
Recently, end-to-end text spotting that aims to detect and recognize text from cluttered images simultaneously has received particularly growing interest in computer vision. Differ…
Deep High-Resolution Representation Learning for Visual Recognition
Jingdong Wang, Ke Sun, Tianheng Cheng +9
High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection. Existing state-of-…
Proposal, Tracking and Segmentation (PTS): A Cascaded Network for Video Object Segmentation
Qiang Zhou, Zilong Huang, Lichao Huang +5
Video object segmentation (VOS) aims at pixel-level object tracking given only the annotations in the first frame. Due to the large visual variations of objects in video and the la…
Densely Connected Search Space for More Flexible Neural Architecture Search
Jiemin Fang, Yuzhu Sun, Qian Zhang +3
Neural architecture search (NAS) has dramatically advanced the development of neural network design. We revisit the search space design in most previous NAS methods and find the nu…
High-Resolution Representations for Labeling Pixels and Regions
Ke Sun, Yang Zhao, Borui Jiang +7
High-resolution representation learning plays an essential role in many vision problems, e.g., pose estimation and semantic segmentation. The high-resolution network (HRNet)~\cite{…