10 citations · 30 across the 5 of their papers we have counts for
8 papers
Learning Domain-invariant Graph for Adaptive Semi-supervised Domain Adaptation with Few Labeled Source Samples
Jinfeng Li, Weifeng Liu, Yicong Zhou +2
Domain adaptation aims to generalize a model from a source domain to tackle tasks in a related but different target domain. Traditional domain adaptation algorithms assume that eno…
Hetero-Center Loss for Cross-Modality Person Re-Identification
Yuanxin Zhu, Zhao Yang, Li Wang +3
Cross-modality person re-identification is a challenging problem which retrieves a given pedestrian image in RGB modality among all the gallery images in infrared modality. The tas…
Knowledge Amalgamation from Heterogeneous Networks by Common Feature Learning
Sihui Luo, Xinchao Wang, Gongfan Fang +3
An increasing number of well-trained deep networks have been released online by researchers and developers, enabling the community to reuse them in a plug-and-play way without acce…
Student Becoming the Master: Knowledge Amalgamation for Joint Scene Parsing, Depth Estimation, and More
Jingwen Ye, Yixin Ji, Xinchao Wang +3
In this paper, we investigate a novel deep-model reusing task. Our goal is to train a lightweight and versatile student model, without human-labelled annotations, that amalgamates…
Semantic Adversarial Network with Multi-scale Pyramid Attention for Video Classification
De Xie, Cheng Deng, Hao Wang +2
Two-stream architecture have shown strong performance in video classification task. The key idea is to learn spatio-temporal features by fusing convolutional networks spatially and…
Ensemble p-Laplacian Regularization for Remote Sensing Image Recognition
Xueqi Ma, Weifeng Liu, Dapeng Tao +1
Recently, manifold regularized semi-supervised learning (MRSSL) received considerable attention because it successfully exploits the geometry of the intrinsic data probability dist…