14 citations · 14 across the 7 of their papers we have counts for
6 papers · 1 filter
GCAgent: Long-Video Understanding via Schematic and Narrative Episodic Memory
Jeong Hun Yeo, Sangyun Chung, Sungjune Park +3
Long-video understanding remains a significant challenge for Multimodal Large Language Models (MLLMs) due to inherent token limitations and the complexity of capturing long-term te…
Enhanced Vision-Language Models for Diverse Sensor Understanding: Cost-Efficient Optimization and Benchmarking
Sangyun Chung, Youngjoon Yu, Se Yeon Kim +2
Large-scale Vision-Language Models (VLMs) have achieved notable progress in aligning visual inputs with text. However, their ability to deeply understand the unique physical proper…
Revisiting Misalignment in Multispectral Pedestrian Detection: A Language-Driven Approach for Cross-modal Alignment Fusion
Taeheon Kim, Sangyun Chung, Youngjoon Yu +1
Multispectral pedestrian detection is a crucial component in various critical applications. However, a significant challenge arises due to the misalignment between these modalities…
Phantom of Latent for Large Language and Vision Models
Byung-Kwan Lee, Sangyun Chung, Chae Won Kim +2
The success of visual instruction tuning has accelerated the development of large language and vision models (LLVMs). Following the scaling laws of instruction-tuned large language…
SPARK: Multi-Vision Sensor Perception and Reasoning Benchmark for Large-scale Vision-Language Models
Youngjoon Yu, Sangyun Chung, Byung-Kwan Lee +1
Large-scale Vision-Language Models (LVLMs) have significantly advanced with text-aligned vision inputs. They have made remarkable progress in computer vision tasks by aligning text…
MSCoTDet: Language-driven Multi-modal Fusion for Improved Multispectral Pedestrian Detection
Taeheon Kim, Sangyun Chung, Damin Yeom +3
Multispectral pedestrian detection is attractive for around-the-clock applications due to the complementary information between RGB and thermal modalities. However, current models…