Seongyu Kim — accepted AI-conference papers

2026

Seeing Through Touch: Tactile-Driven Visual Localization of Material Regions

Seongyu Kim, Seungwoo Lee, Hyeonggon Ryu, Joon Son Chung, Arda Senocak

CVPR 2026

We address the problem of tactile localization, where the goal is to identify image regions that share the same material properties as a tactile input. Existing visuo-tactile methods rely on global alignment and thus fail to capture the fine-grained local correspondences required for this task. The

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2025

Seeing Speech and Sound: Distinguishing and Locating Audio Sources in Visual Scenes

Hyeonggon Ryu, Seongyu Kim, Joon Son Chung, Arda Senocak

CVPR 2025poster

We present a unified model capable of simultaneously grounding both spoken language and non-speech sounds within a visual scene, addressing key limitations in current audio-visual grounding models. Existing approaches are typically limited to handling either speech or non-speech sounds independently…

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