5 papers
Mitigating State Aliasing in Vision-Language-Action Models via Inverse Dynamics Learning
Kyujin Lee, Injae Kim, Jihwan Park +3
Vision-Language-Action (VLA) models have emerged as a promising framework that unifies perception, reasoning, and control for robot manipulation by adapting pretrained vision-langu…
RegFormer: Transferable Relational Grounding for Efficient Weakly-Supervised Human-Object Interaction Detection
Jihwan Park, Chanhyeong Yang, Jinyoung Park +2
Weakly-supervised Human-Object Interaction (HOI) detection is essential for scalable scene understanding, as it learns interactions from only image-level annotations. Due to the la…
Transferable Model-agnostic Vision-Language Model Adaptation for Efficient Weak-to-Strong Generalization
Jihwan Park, Taehoon Song, Sanghyeok Lee +2
Vision-Language Models (VLMs) have been widely used in various visual recognition tasks due to their remarkable generalization capabilities. As these models grow in size and comple…
Visual Diversity and Region-aware Prompt Learning for Zero-shot HOI Detection
Chanhyeong Yang, Taehoon Song, Jihwan Park +1
Zero-shot Human-Object Interaction detection aims to localize humans and objects in an image and recognize their interaction, even when specific verb-object pairs are unseen during…
Super-class guided Transformer for Zero-Shot Attribute Classification
Sehyung Kim, Chanhyeong Yang, Jihwan Park +2
Attribute classification is crucial for identifying specific characteristics within image regions. Vision-Language Models (VLMs) have been effective in zero-shot tasks by leveragin…