11 citations · 27 across the 8 of their papers we have counts for
8 papers · 1 filter
Multi-view Adversarial Discriminator: Mine the Non-causal Factors for Object Detection in Unseen Domains
Mingjun Xu, Lingyun Qin, Weijie Chen +2
Domain shift degrades the performance of object detection models in practical applications. To alleviate the influence of domain shift, plenty of previous work try to decouple and…
Attention Diversification for Domain Generalization
Rang Meng, Xianfeng Li, Weijie Chen +7
Convolutional neural networks (CNNs) have demonstrated gratifying results at learning discriminative features. However, when applied to unseen domains, state-of-the-art models are…
Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptation
Weijie Chen, Luojun Lin, Shicai Yang +4
It is a strong prerequisite to access source data freely in many existing unsupervised domain adaptation approaches. However, source data is agnostic in many practical scenarios du…
Box Re-Ranking: Unsupervised False Positive Suppression for Domain Adaptive Pedestrian Detection
Weijie Chen, Yilu Guo, Shicai Yang +7
False positive is one of the most serious problems brought by agnostic domain shift in domain adaptive pedestrian detection. However, it is impossible to label each box in countles…
A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data
Xianfeng Li, Weijie Chen, Di Xie +4
Unsupervised domain adaptation (UDA) assumes that source and target domain data are freely available and usually trained together to reduce the domain gap. However, considering the…
Unsupervised Image Classification for Deep Representation Learning
Weijie Chen, Shiliang Pu, Di Xie +3
Deep clustering against self-supervised learning is a very important and promising direction for unsupervised visual representation learning since it requires little domain knowled…