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20212023
most citedCausality Inspired Representation Learning for Domain Generalization

13 citations · 44 across the 11 of their papers we have counts for

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Showing 2022Show all

6 papers · 1 filter

cs.CV2022★ 1 cited

VBLC: Visibility Boosting and Logit-Constraint Learning for Domain Adaptive Semantic Segmentation under Adverse Conditions

Mingjia Li, Binhui Xie, Shuang Li +2

Generalizing models trained on normal visual conditions to target domains under adverse conditions is demanding in the practical systems. One prevalent solution is to bridge the do…

cs.CV2022

Making the Best of Both Worlds: A Domain-Oriented Transformer for Unsupervised Domain Adaptation

Wenxuan Ma, Jinming Zhang, Shuang Li +3

Extensive studies on Unsupervised Domain Adaptation (UDA) have propelled the deployment of deep learning from limited experimental datasets into real-world unconstrained domains. M…

cs.CV2022★ 2 cited

ROMA: Cross-Domain Region Similarity Matching for Unpaired Nighttime Infrared to Daytime Visible Video Translation

Zhenjie Yu, Kai Chen, Shuang Li +3

Infrared cameras are often utilized to enhance the night vision since the visible light cameras exhibit inferior efficacy without sufficient illumination. However, infrared data po…

cs.CV2022★ 2 cited

Improving Transferability for Domain Adaptive Detection Transformers

Kaixiong Gong, Shuang Li, Shugang Li +3

DETR-style detectors stand out amongst in-domain scenarios, but their properties in domain shift settings are under-explored. This paper aims to build a simple but effective baseli…

cs.CV2022★ 5 cited

SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation

Binhui Xie, Shuang Li, Mingjia Li +3

Domain adaptive semantic segmentation attempts to make satisfactory dense predictions on an unlabeled target domain by utilizing the supervised model trained on a labeled source do…

cs.LG2022★ 13 cited

Causality Inspired Representation Learning for Domain Generalization

Fangrui Lv, Jian Liang, Shuang Li +4

Domain generalization (DG) is essentially an out-of-distribution problem, aiming to generalize the knowledge learned from multiple source domains to an unseen target domain. The ma…