2 citations · 4 across the 10 of their papers we have counts for
10 papers
Robust Grounding with MLLMs Against Occlusion and Small Objects via Language-Guided Semantic Cues
Beomchan Park, Seongho Kim, Hyunjun Kim +2
While Multimodal Large Language Models (MLLMs) have enhanced grounding capabilities in general scenes, their robustness in crowded scenes remains underexplored. Crowded scenes enta…
ReFoCUS: Reinforcement-guided Frame Optimization for Contextual Understanding
Hosu Lee, Junho Kim, Hyunjun Kim +1
Recent progress in Large Multi-modal Models (LMMs) has enabled effective vision-language reasoning, yet the ability to video understanding remains constrained by suboptimal frame s…
Language-guided Learning for Object Detection Tackling Multiple Variations in Aerial Images
Sungjune Park, Hyunjun Kim, Beomchan Park +1
Despite recent advancements in computer vision research, object detection in aerial images still suffers from several challenges. One primary challenge to be mitigated is the prese…
DIP-R1: Deep Inspection and Perception with RL Looking Through and Understanding Complex Scenes
Sungjune Park, Hyunjun Kim, Junho Kim +2
MLLMs have demonstrated significant visual understanding capabilities, yet their fine-grained visual perception in complex real-world scenarios, such as densely crowded public area…
Look Every Frame All at Once: Video-Mamba for Efficient Long-form Video Understanding with Multi-Axis Gradient Checkpointing
Hosu Lee, Junho Kim, Hyunjun Kim +1
With the growing scale and complexity of video data, efficiently processing long video sequences poses significant challenges due to the quadratic increase in memory and computatio…
SALOVA: Segment-Augmented Long Video Assistant for Targeted Retrieval and Routing in Long-Form Video Analysis
Junho Kim, Hyunjun Kim, Hosu Lee +1
Despite advances in Large Multi-modal Models, applying them to long and untrimmed video content remains challenging due to limitations in context length and substantial memory over…
Personalized Lip Reading: Adapting to Your Unique Lip Movements with Vision and Language
Jeong Hun Yeo, Chae Won Kim, Hyunjun Kim +4
Lip reading aims to predict spoken language by analyzing lip movements. Despite advancements in lip reading technologies, performance degrades when models are applied to unseen spe…
CODE: Contrasting Self-generated Description to Combat Hallucination in Large Multi-modal Models
Junho Kim, Hyunjun Kim, Yeonju Kim +1
Large Multi-modal Models (LMMs) have recently demonstrated remarkable abilities in visual context understanding and coherent response generation. However, alongside these advanceme…
Robust Pedestrian Detection via Constructing Versatile Pedestrian Knowledge Bank
Sungjune Park, Hyunjun Kim, Yong Man Ro
Pedestrian detection is a crucial field of computer vision research which can be adopted in various real-world applications (e.g., self-driving systems). However, despite noticeabl…
Integrating Language-Derived Appearance Elements with Visual Cues in Pedestrian Detection
Sungjune Park, Hyunjun Kim, Yong Man Ro
Large language models (LLMs) have shown their capabilities in understanding contextual and semantic information regarding knowledge of instance appearances. In this paper, we intro…