42 citations · 64 across the 6 of their papers we have counts for
7 papers
Global Meets Local: Effective Multi-Label Image Classification via Category-Aware Weak Supervision
Jiawei Zhan, Jun Liu, Wei Tang +8
Multi-label image classification, which can be categorized into label-dependency and region-based methods, is a challenging problem due to the complex underlying object layouts. Al…
Rethinking the Metric in Few-shot Learning: From an Adaptive Multi-Distance Perspective
Jinxiang Lai, Siqian Yang, Guannan Jiang +9
Few-shot learning problem focuses on recognizing unseen classes given a few labeled images. In recent effort, more attention is paid to fine-grained feature embedding, ignoring the…
A Coarse-to-Fine Instance Segmentation Network with Learning Boundary Representation
Feng Luo, Bin-Bin Gao, Jiangpeng Yan +1
Boundary-based instance segmentation has drawn much attention since of its attractive efficiency. However, existing methods suffer from the difficulty in long-distance regression.…
SCNet: Enhancing Few-Shot Semantic Segmentation by Self-Contrastive Background Prototypes
Jiacheng Chen, Bin-Bin Gao, Zongqing Lu +3
Few-shot semantic segmentation aims to segment novel-class objects in a query image with only a few annotated examples in support images. Most of advanced solutions exploit a metri…
Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors
Hong-Yu Zhou, Bin-Bin Gao, Jianxin Wu
Object detection aims at high speed and accuracy simultaneously. However, fast models are usually less accurate, while accurate models cannot satisfy our need for speed. A fast mod…
Sunrise or Sunset: Selective Comparison Learning for Subtle Attribute Recognition
Hong-Yu Zhou, Bin-Bin Gao, Jianxin Wu
The difficulty of image recognition has gradually increased from general category recognition to fine-grained recognition and to the recognition of some subtle attributes such as t…