6 papers
BFMD: A Full-Match Badminton Dense Dataset for Dense Shot Captioning
Ning Ding, Keisuke Fujii, Toru Tamaki
Understanding tactical dynamics in badminton requires analyzing entire matches rather than isolated clips. However, existing badminton datasets mainly focus on short clips or task-…
Shot2Tactic-Caption: Multi-Scale Captioning of Badminton Videos for Tactical Understanding
Ning Ding, Keisuke Fujii, Toru Tamaki
Tactical understanding in badminton involves interpreting not only individual actions but also how tactics are dynamically executed over time. In this paper, we propose \textbf{Sho…
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…
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…
MoExDA: Domain Adaptation for Edge-based Action Recognition
Takuya Sugimoto, Ning Ding, Toru Tamaki
Modern action recognition models suffer from static bias, leading to reduced generalization performance. In this paper, we propose MoExDA, a lightweight domain adaptation between R…
Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning
Yusaku Takama, Ning Ding, Tatsuya Yokota +1
Existing architectures of multi-domain learning have two types of adapters: shared LoRA for all domains and domain-specific LoRA for each particular domain. However, it remains unc…