14 citations · 33 across the 26 of their papers we have counts for
13 papers · 1 filter
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…
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…
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…
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…
TroL: Traversal of Layers for Large Language and Vision Models
Byung-Kwan Lee, Sangyun Chung, Chae Won Kim +2
Large language and vision models (LLVMs) have been driven by the generalization power of large language models (LLMs) and the advent of visual instruction tuning. Along with scalin…
Meteor: Mamba-based Traversal of Rationale for Large Language and Vision Models
Byung-Kwan Lee, Chae Won Kim, Beomchan Park +1
The rapid development of large language and vision models (LLVMs) has been driven by advances in visual instruction tuning. Recently, open-source LLVMs have curated high-quality vi…
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…
Causal Mode Multiplexer: A Novel Framework for Unbiased Multispectral Pedestrian Detection
Taeheon Kim, Sebin Shin, Youngjoon Yu +2
RGBT multispectral pedestrian detection has emerged as a promising solution for safety-critical applications that require day/night operations. However, the modality bias problem r…
Persona Extraction Through Semantic Similarity for Emotional Support Conversation Generation
Seunghee Han, Se Jin Park, Chae Won Kim +1
Providing emotional support through dialogue systems is becoming increasingly important in today's world, as it can support both mental health and social interactions in many conve…
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…
What if...?: Thinking Counterfactual Keywords Helps to Mitigate Hallucination in Large Multi-modal Models
Junho Kim, Yeon Ju Kim, Yong Man Ro
This paper presents a way of enhancing the reliability of Large Multi-modal Models (LMMs) in addressing hallucination, where the models generate cross-modal inconsistent responses.…
MoAI: Mixture of All Intelligence for Large Language and Vision Models
Byung-Kwan Lee, Beomchan Park, Chae Won Kim +1
The rise of large language models (LLMs) and instruction tuning has led to the current trend of instruction-tuned large language and vision models (LLVMs). This trend involves eith…
CoLLaVO: Crayon Large Language and Vision mOdel
Byung-Kwan Lee, Beomchan Park, Chae Won Kim +1
The remarkable success of Large Language Models (LLMs) and instruction tuning drives the evolution of Vision Language Models (VLMs) towards a versatile general-purpose model. Yet,…