10 papers
VisLens: Single-Pass Interpretable Visual Search for Multimodal LLMs
Jingyi He, Sanghwan Kim, Zeynep Akata
Multimodal large language models (MLLMs) struggle with fine-grained Visual Search, the task of locating small or rare objects in high-resolution images. Existing remedies fall into…
From Accuracy to Visual Dependence: Auditing and Filtering Modality Collapse in Traffic VideoQA
Sena Korkut, María Alejandra Bravo Sarmiento, Sanghwan Kim +1
High benchmark accuracy does not guarantee genuine use of visual evidence. We study this problem in traffic accident Video Question Answering (VideoQA), where correct answers shoul…
Instance-Optimal Estimation with Multiple LLM Judges on a Budget
Junghyun Lee, Sanghwa Kim, Yassir Jedra +2
Evaluating large language models increasingly relies on LLM-as-a-judge protocols, but such evaluations remain costly: different judges have different prices and reliabilities, and…
When Sinks Help or Hurt: Unified Framework for Attention Sink in Large Vision-Language Models
Jiho Choi, Jaemin Kim, Sanghwan Kim +2
Attention sinks are defined as tokens that attract disproportionate attention. While these have been studied in single modality transformers, their cross-modal impact in Large Visi…
SafeFlow: Real-Time Text-Driven Humanoid Whole-Body Control via Physics-Guided Rectified Flow and Selective Safety Gating
Hanbyel Cho, Sang-Hun Kim, Jeonguk Kang +1
Recent advances in real-time interactive text-driven motion generation have enabled humanoids to perform diverse behaviors. However, kinematics-only generators often exhibit physic…
FINER: MLLMs Hallucinate under Fine-grained Negative Queries
Rui Xiao, Sanghwan Kim, Yongqin Xian +2
Multimodal large language models (MLLMs) struggle with hallucinations, particularly with fine-grained queries, a challenge underrepresented by existing benchmarks that focus on coa…
From Drop-off to Recovery: A Mechanistic Analysis of Segmentation in MLLMs
Boyong Wu, Sanghwan Kim, Zeynep Akata
Multimodal Large Language Models (MLLMs) are increasingly applied to pixel-level vision tasks, yet their intrinsic capacity for spatial understanding remains poorly understood. We…
Training-free Uncertainty Guidance for Complex Visual Tasks with MLLMs
Sanghwan Kim, Rui Xiao, Stephan Alaniz +2
Multimodal Large Language Models (MLLMs) often struggle with fine-grained perception, such as identifying small objects in high-resolution images or detecting key moments in long v…
FLAIR: VLM with Fine-grained Language-informed Image Representations
Rui Xiao, Sanghwan Kim, Mariana-Iuliana Georgescu +2
CLIP has shown impressive results in aligning images and texts at scale. However, its ability to capture detailed visual features remains limited because CLIP matches images and te…
COSMOS: Cross-Modality Self-Distillation for Vision Language Pre-training
Sanghwan Kim, Rui Xiao, Mariana-Iuliana Georgescu +2
Vision-Language Models (VLMs) trained with contrastive loss have achieved significant advancements in various vision and language tasks. However, the global nature of the contrasti…