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Similarity-preserving Image-image Domain Adaptation for Person Re-identification
Weijian Deng, Liang Zheng, Qixiang Ye +2
This article studies the domain adaptation problem in person re-identification (re-ID) under a "learning via translation" framework, consisting of two components, 1) translating th…
SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation
Xiaolin Zhang, Yunchao Wei, Yi Yang +1
One-shot image semantic segmentation poses a challenging task of recognizing the object regions from unseen categories with only one annotated example as supervision. In this paper…
Every Node Counts: Self-Ensembling Graph Convolutional Networks for Semi-Supervised Learning
Yawei Luo, Tao Guan, Junqing Yu +2
Graph convolutional network (GCN) provides a powerful means for graph-based semi-supervised tasks. However, as a localized first-order approximation of spectral graph convolution,…
Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation
Yawei Luo, Liang Zheng, Tao Guan +2
We consider the problem of unsupervised domain adaptation in semantic segmentation. The key in this campaign consists in reducing the domain shift, i.e., enforcing the data distrib…
Query Attack via Opposite-Direction Feature:Towards Robust Image Retrieval
Zhedong Zheng, Liang Zheng, Yi Yang +1
Most existing works of adversarial samples focus on attacking image recognition models, while little attention is paid to the image retrieval task. In this paper, we identify two i…
Self-produced Guidance for Weakly-supervised Object Localization
Xiaolin Zhang, Yunchao Wei, Guoliang Kang +2
Weakly supervised methods usually generate localization results based on attention maps produced by classification networks. However, the attention maps exhibit the most discrimina…