5 papers
IWP: Token Pruning as Implicit Weight Pruning in Large Vision Language Models
Dong-Jae Lee, Sunghyun Baek, Junmo Kim
Large Vision Language Models show impressive performance across image and video understanding tasks, yet their computational cost grows rapidly with the number of visual tokens. Ex…
ARGOS: Who, Where, and When in Agentic Multi-Camera Person Search
Myungchul Kim, Kwanyong Park, Junmo Kim +1
We introduce ARGOS, the first benchmark and framework that reformulates multi-camera person search as an interactive reasoning problem requiring an agent to plan, question, and eli…
IMSE: Intrinsic Mixture of Spectral Experts Fine-tuning for Test-Time Adaptation
Sunghyun Baek, Jaemyung Yu, Seunghee Koh +3
Test-time adaptation (TTA) has been widely explored to prevent performance degradation when test data differ from the training distribution. However, fully leveraging the rich repr…
Beyond Semantics: Disentangling Information Scope in Sparse Autoencoders for CLIP
Yusung Ro, Jaehyun Choi, Junmo Kim
Sparse Autoencoders (SAEs) have emerged as a powerful tool for interpreting the internal representations of CLIP vision encoders, yet existing analyses largely focus on the semanti…
Video Diffusion Models Excel at Tracking Similar-Looking Objects Without Supervision
Chenshuang Zhang, Kang Zhang, Joon Son Chung +3
Distinguishing visually similar objects by their motion remains a critical challenge in computer vision. Although supervised trackers show promise, contemporary self-supervised tra…