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cs.CV2026

Why Can't I Open My Drawer? Mitigating Object-Driven Shortcuts in Zero-Shot Compositional Action Recognition

Geo Ahn, Inwoong Lee, Taeoh Kim +3

Zero-Shot Compositional Action Recognition (ZS-CAR) requires recognizing novel verb-object combinations composed of previously observed primitives. In this work, we tackle a key fa…

cs.CV2026

EVIDENT: Routing MLLM Adaptation through Entity-Grounded Visual Evidence for Cross-Domain Video Temporal Grounding

Geo Ahn, Jiwook Han, Youngrae Kim +2

Fine-tuning MLLMs for Video Temporal Grounding (VTG) often improves in-domain performance but degrades sharply under domain shift. In this work, we find that this failure is primar…

cs.CV2026

SlotVTG: Object-Centric Adapter for Generalizable Video Temporal Grounding

Jiwook Han, Geo Ahn, Youngrae Kim +1

Multimodal Large Language Models (MLLMs) have shown strong performance on Video Temporal Grounding (VTG). However, their coarse recognition capabilities are insufficient for fine-g…

cs.CV2024

PCEvE: Part Contribution Evaluation Based Model Explanation for Human Figure Drawing Assessment and Beyond

Jongseo Lee, Geo Ahn, Seong Tae Kim +1

For automatic human figure drawing (HFD) assessment tasks, such as diagnosing autism spectrum disorder (ASD) using HFD images, the clarity and explainability of a model decision ar…

cs.CV2024

DEVIAS: Learning Disentangled Video Representations of Action and Scene

Kyungho Bae, Geo Ahn, Youngrae Kim +1

Video recognition models often learn scene-biased action representation due to the spurious correlation between actions and scenes in the training data. Such models show poor perfo…