2 papers
cs.CV2025
Disentangling Static and Dynamic Information for Reducing Static Bias in Action Recognition
Masato Kobayashi, Ning Ding, Toru Tamaki
Action recognition models rely excessively on static cues rather than dynamic human motion, which is known as static bias. This bias leads to poor performance in real-world applica…
cs.LG2025
The 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real): Methods and Results
Qiuyu Chen, Xin Jin, Yue Song +45
This paper reviews the 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real), held in conjunction with ICCV 2025. The workshop a…