This research tackles the challenge of video unsupervised domain adaptation (VUDA) in human action recognition, focusing on deploying models trained in well-lit conditions to poorly-lit environments. Current unsupervi...
详细信息
This research tackles the challenge of video unsupervised domain adaptation (VUDA) in human action recognition, focusing on deploying models trained in well-lit conditions to poorly-lit environments. Current unsupervi...
详细信息
ISBN:
(数字)9798350368604
ISBN:
(纸本)9798350368611
This research tackles the challenge of video unsupervised domain adaptation (VUDA) in human action recognition, focusing on deploying models trained in well-lit conditions to poorly-lit environments. Current unsupervised domain adaptation (UDA) techniques mitigate performance degradation by aligning source and target domains through adversarial training. However, these approaches fail to enhance low-light images, resulting in information loss and suboptimal performance in dark settings. To address this issue, we introduce Pseudo-Augmentation Entropy Domain Adaptation (PANDA), an entropy theory-based method that simultaneously complements unsupervised adversarial alignment by incorporating image enhancement as an auxiliary task. Evaluations on the HMDB-ARID datasets demonstrate that proposed PANDA outperforms standard VUDA baselines, boosting action recognition accuracy by an average of 6.3%.
暂无评论