VBLC: visibility boosting and logit-constraint learning for domain adaptive semantic segmentation under adverse conditions

M Li, B Xie, S Li, CH Liu, X Cheng - … of the AAAI Conference on Artificial …, 2023 - ojs.aaai.org
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 …
被引用次数：8 相关文章
[PDF] researchgate.net

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

M Li, B Xie, S Li, CH Liu, X Cheng - 2023 - researchgate.net
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 …

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

M Li, B Xie, S Li, CH Liu, X Cheng - arXiv e-prints, 2022 - ui.adsabs.harvard.edu
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 …

VBLC: visibility boosting and logit-constraint learning for domain adaptive semantic segmentation under adverse conditions

M Li, B Xie, S Li, CH Liu, X Cheng - Proceedings of the Thirty-Seventh …, 2023 - dl.acm.org
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 …

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

M Li, B Xie, S Li, CH Liu, X Cheng - arXiv preprint arXiv:2211.12256, 2022 - arxiv.org
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 …