2.4k citations · 3.3k across the 23 of their papers we have counts for
7 papers · 1 filter
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…
RDSNet: A New Deep Architecture for Reciprocal Object Detection and Instance Segmentation
Shaoru Wang, Yongchao Gong, Junliang Xing +3
Object detection and instance segmentation are two fundamental computer vision tasks. They are closely correlated but their relations have not yet been fully explored in most previ…
High Performance Visual Object Tracking with Unified Convolutional Networks
Zheng Zhu, Wei Zou, Guan Huang +2
Convolutional neural networks (CNN) based tracking approaches have shown favorable performance in recent benchmarks. Nonetheless, the chosen CNN features are always pre-trained in…
Object Detection in Video with Spatial-temporal Context Aggregation
Hao Luo, Lichao Huang, Han Shen +3
Recent cutting-edge feature aggregation paradigms for video object detection rely on inferring feature correspondence. The feature correspondence estimation problem is fundamentall…
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…
Mask Scoring R-CNN
Zhaojin Huang, Lichao Huang, Yongchao Gong +2
Letting a deep network be aware of the quality of its own predictions is an interesting yet important problem. In the task of instance segmentation, the confidence of instance clas…
EAT-NAS: Elastic Architecture Transfer for Accelerating Large-scale Neural Architecture Search
Jiemin Fang, Yukang Chen, Xinbang Zhang +5
Neural architecture search (NAS) methods have been proposed to release human experts from tedious architecture engineering. However, most current methods are constrained in small-s…