12 citations · 13 across the 2 of their papers we have counts for
6 papers
Multi-scale Matching Networks for Semantic Correspondence
Dongyang Zhao, Ziyang Song, Zhenghao Ji +3
Deep features have been proven powerful in building accurate dense semantic correspondences in various previous works. However, the multi-scale and pyramidal hierarchy of convoluti…
Label-PEnet: Sequential Label Propagation and Enhancement Networks for Weakly Supervised Instance Segmentation
Weifeng Ge, Sheng Guo, Weilin Huang +1
Weakly-supervised instance segmentation aims to detect and segment object instances precisely, given imagelevel labels only. Unlike previous methods which are composed of multiple…
Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification from the Bottom Up
Weifeng Ge, Xiangru Lin, Yizhou Yu
Given a training dataset composed of images and corresponding category labels, deep convolutional neural networks show a strong ability in mining discriminative parts for image cla…
Deep Metric Learning with Hierarchical Triplet Loss
Weifeng Ge, Weilin Huang, Dengke Dong +1
We present a novel hierarchical triplet loss (HTL) capable of automatically collecting informative training samples (triplets) via a defined hierarchical tree that encodes global c…
Image Super-Resolution via Deterministic-Stochastic Synthesis and Local Statistical Rectification
Weifeng Ge, Bingchen Gong, Yizhou Yu
Single image superresolution has been a popular research topic in the last two decades and has recently received a new wave of interest due to deep neural networks. In this paper,…
Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning
Weifeng Ge, Sibei Yang, Yizhou Yu
Supervised object detection and semantic segmentation require object or even pixel level annotations. When there exist image level labels only, it is challenging for weakly supervi…