2 citations · 2 across the 14 of their papers we have counts for
17 papers
Grounding Confusable Movie Moments
Dong-Hee Kim, Seonwoo Choi, Changbeen Kim +5
Characters, locations, and actions recur throughout a movie, so many of its moments differ only in fine-grained details. Grounding such a moment in a full-length movie requires res…
WearableQA: A Benchmark for Health Reasoning over Real-World Wearable Data
Ji Soo Lee, Xilun Chen, Pierce Chuang +5
Recent advances in wearable sensing enable continuous monitoring of physiological and behavioral signals, yet existing benchmarks rarely evaluate whether AI systems can reason over…
Beyond Visual Boundaries: Rethinking Scene Segmentation for Movie RAG
Dong-Hee Kim, Seonwoo Choi, Changbeen Kim +6
Understanding long-form video remains a fundamental challenge for multimodal large language models (MLLMs). Sparse frame sampling fails to capture fine-grained visual details, whil…
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…
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…
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…
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…
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…
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…
MoE-GRPO: Optimizing Mixture-of-Experts via Reinforcement Learning in Vision-Language Models
Dohwan Ko, Jinyoung Park, Seoung Choi +3
Mixture-of-Experts (MoE) has emerged as an effective approach to reduce the computational overhead of Transformer architectures by sparsely activating a subset of parameters for ea…
Relevance-aware Multi-context Contrastive Decoding for Retrieval-augmented Visual Question Answering
Jongha Kim, Byungoh Ko, Jeehye Na +2
Despite the remarkable capabilities of Large Vision Language Models (LVLMs), they still lack detailed knowledge about specific entities. Retrieval-augmented Generation (RAG) is a w…
Captioning for Text-Video Retrieval via Dual-Group Direct Preference Optimization
Ji Soo Lee, Byungoh Ko, Jaewon Cho +3
In text-video retrieval, auxiliary captions are often used to enhance video understanding, bridging the gap between the modalities. While recent advances in multi-modal large langu…
Bidirectional Likelihood Estimation with Multi-Modal Large Language Models for Text-Video Retrieval
Dohwan Ko, Ji Soo Lee, Minhyuk Choi +2
Text-Video Retrieval aims to find the most relevant text (or video) candidate given a video (or text) query from large-scale online databases. Recent work leverages multi-modal lar…
ST-VLM: Kinematic Instruction Tuning for Spatio-Temporal Reasoning in Vision-Language Models
Dohwan Ko, Sihyeon Kim, Yumin Suh +4
Spatio-temporal reasoning is essential in understanding real-world environments in various fields, eg, autonomous driving and sports analytics. Recent advances have improved the sp…
VidChain: Chain-of-Tasks with Metric-based Direct Preference Optimization for Dense Video Captioning
Ji Soo Lee, Jongha Kim, Jeehye Na +2
Despite the advancements of Video Large Language Models (VideoLLMs) in various tasks, they struggle with fine-grained temporal understanding, such as Dense Video Captioning (DVC).…
LLaMo: Large Language Model-based Molecular Graph Assistant
Jinyoung Park, Minseong Bae, Dohwan Ko +1
Large Language Models (LLMs) have demonstrated remarkable generalization and instruction-following capabilities with instruction tuning. The advancements in LLMs and instruction tu…
Large Language Models are Temporal and Causal Reasoners for Video Question Answering
Dohwan Ko, Ji Soo Lee, Wooyoung Kang +2
Large Language Models (LLMs) have shown remarkable performances on a wide range of natural language understanding and generation tasks. We observe that the LLMs provide effective p…