collaborators

12 papers

cs.CV2026

Context Blindness in DPO: Mitigating Object Hallucination in MLLMs via Context-Calibrated Preference Optimization

Byungoh Ko, Jinyoung Park, Jongha Kim +3

Multimodal large language models (MLLMs) have made rapid progress, yet they still exhibit object hallucination, generating plausible but incorrect descriptions that are inconsisten…

cs.CV2026

VideoSearch-R1: Iterative Video Retrieval and Reasoning via Soft Query Refinement

Seohyun Lee, Seoung Choi, Dohwan Ko +2

As video corpora continue to expand in both scale and task complexity, there is increasing demand for approaches that retrieve relevant videos from large-scale corpora (inter-video…

cs.CV2026

GLINT: Sparsely Gated Vision-Language Alignment for Fine-Grained Radiology Representations

Jonggwon Park, Seongeun Lee, Junhyun Park +6

Vision-language models (VLMs) for radiology have emerged as a scalable paradigm by leveraging image-report pairs naturally produced in clinical workflows. However, this pairing rev…

cs.CV2026

Retrieve What's Missing: Coverage-Maximizing Retrieval for Consistent Long Video Generation

Minseok Joo, Dogyun Park, Taehoon Lee +2

Maintaining long-term geometric consistency remains challenging for long-horizon autoregressive video generation. Memory-augmented generative models address this by retrieving hist…

cs.CV2026

DocPrune:Efficient Document Question Answering via Background, Question, and Comprehension-aware Token Pruning

Joonmyung Choi, Sanghyeok Lee, Jongha Kim +4

Recent advances in vision-language models have demonstrated remarkable performance across diverse multi-modal tasks, including document question answering that leverages structured…

cs.CV2026

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…