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20172022
most citedA Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data

11 citations · 36 across the 7 of their papers we have counts for

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7 papers · 1 filter

cs.CV2023

1st Place Solution for ECCV 2022 OOD-CV Challenge Image Classification Track

Yilu Guo, Xingyue Shi, Weijie Chen +4

OOD-CV challenge is an out-of-distribution generalization task. In this challenge, our core solution can be summarized as that Noisy Label Learning Is A Strong Test-Time Domain Ada…

cs.CV20223 cited

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…

cs.CV2021

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…

cs.CV20213 cited

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…

cs.CV202011 cited

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…

cs.CV20202 cited

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…