14 citations · 33 across the 26 of their papers we have counts for
19 papers · 1 filter
From Coordinates to Candidate Regions: Temporal Change Localization via Region Selection in Remote Sensing Multimodal LLMs
Juwan Chung, Sungjune Park, Yeongyun Kim +1
Remote sensing multimodal large language models (RS-MLLMs) have advanced scene understanding and visual question answering over satellite imagery, yet localizing specific objects o…
PRISM: Predictive Recomposition via Semantic Latent Decomposition for View-invariant Video Representation Learning
Youngchae Chee, Hosu Lee, Sungjune Park +2
Cross-view video representation learning aims to capture viewpoint-invariant action semantics despite substantial appearance changes across egocentric and exocentric videos. Howeve…
Robust Egocentric Visual Attention Prediction Through Language-guided Scene Context-aware Learning
Sungjune Park, Hongda Mao, Qingshuang Chen +2
As the demand for analyzing egocentric videos grows, egocentric visual attention prediction, anticipating where a camera wearer will attend, has garnered increasing attention. Howe…
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…
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…
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…
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,…
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…
Causal Unsupervised Semantic Segmentation
Junho Kim, Byung-Kwan Lee, Yong Man Ro
Unsupervised semantic segmentation aims to achieve high-quality semantic grouping without human-labeled annotations. With the advent of self-supervised pre-training, various framew…
Robust Proxy: Improving Adversarial Robustness by Robust Proxy Learning
Hong Joo Lee, Yong Man Ro
Recently, it has been widely known that deep neural networks are highly vulnerable and easily broken by adversarial attacks. To mitigate the adversarial vulnerability, many defense…
Advancing Adversarial Training by Injecting Booster Signal
Hong Joo Lee, Youngjoon Yu, Yong Man Ro
Recent works have demonstrated that deep neural networks (DNNs) are highly vulnerable to adversarial attacks. To defend against adversarial attacks, many defense strategies have be…