5 papers
Adversarial Reinforcement Learning Framework for ESP Cheater Simulation
Inkyu Park, Jeong-Gwan Lee, Taehwan Kwon +4
Extra-Sensory Perception (ESP) cheats, which reveal hidden in-game information such as enemy locations, are difficult to detect because their effects are not directly observable in…
Can Synthetic Images Conquer Forgetting? Beyond Unexplored Doubts in Few-Shot Class-Incremental Learning
Junsu Kim, Yunhoe Ku, Seungryul Baek
Few-shot class-incremental learning (FSCIL) is challenging due to extremely limited training data; while aiming to reduce catastrophic forgetting and learn new information. We prop…
Revisiting Reliability in the Reasoning-based Pose Estimation Benchmark
Junsu Kim, Naeun Kim, Jaeho Lee +3
The reasoning-based pose estimation (RPE) benchmark has emerged as a widely adopted evaluation standard for pose-aware multimodal large language models (MLLMs). Despite its signifi…
Beyond Synthetic Replays: Turning Diffusion Features into Few-Shot Class-Incremental Learning Knowledge
Junsu Kim, Yunhoe Ku, Dongyoon Han +1
Few-shot class-incremental learning (FSCIL) is challenging due to extremely limited training data while requiring models to acquire new knowledge without catastrophic forgetting. R…
B-RIGHT: Benchmark Re-evaluation for Integrity in Generalized Human-Object Interaction Testing
Yoojin Jang, Junsu Kim, Hayeon Kim +4
Human-object interaction (HOI) is an essential problem in artificial intelligence (AI) which aims to understand the visual world that involves complex relationships between humans…