1 citations · 1 across the 10 of their papers we have counts for
5 papers · 1 filter
MA-EgoQA: Question Answering over Egocentric Videos from Multiple Embodied Agents
Kangsan Kim, Yanlai Yang, Suji Kim +4
As embodied models become powerful, humans will collaborate with multiple embodied AI agents at their workplace or home in the future. To ensure better communication between human…
WorldMM: Dynamic Multimodal Memory Agent for Long Video Reasoning
Woongyeong Yeo, Kangsan Kim, Jaehong Yoon +1
Recent advances in video large language models have demonstrated strong capabilities in understanding short clips. However, scaling them to hours- or days-long videos remains highl…
HoliSafe: Holistic Safety Benchmarking and Modeling for Vision-Language Model
Youngwan Lee, Kangsan Kim, Kwanyong Park +5
Despite emerging efforts to enhance the safety of Vision-Language Models (VLMs), current approaches face two main shortcomings. 1) Existing safety-tuning datasets and benchmarks on…
VideoRAG: Retrieval-Augmented Generation over Video Corpus
Soyeong Jeong, Kangsan Kim, Jinheon Baek +1
Retrieval-Augmented Generation (RAG) is a powerful strategy for improving the factual accuracy of models by retrieving external knowledge relevant to queries and incorporating it i…
VideoICL: Confidence-based Iterative In-context Learning for Out-of-Distribution Video Understanding
Kangsan Kim, Geon Park, Youngwan Lee +2
Recent advancements in video large multimodal models (LMMs) have significantly improved their video understanding and reasoning capabilities. However, their performance drops on ou…