5 papers · 1 filter
MultihopSpatial: Multi-hop Compositional Spatial Reasoning Benchmark for Vision-Language Model
Youngwan Lee, Soojin Jang, Yoorhim Cho +3
Spatial reasoning is foundational for Vision-Language Models (VLMs), particularly when deployed as Vision-Language-Action (VLA) agents in physical environments. However, existing b…
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…
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…
SIA: Enhancing Safety via Intent Awareness for Vision-Language Models
Youngjin Na, Sangheon Jeong, Youngwan Lee +3
With the growing deployment of Vision-Language Models (VLMs) in real-world applications, previously overlooked safety risks are becoming increasingly evident. In particular, seemin…
PC-LoRA: Low-Rank Adaptation for Progressive Model Compression with Knowledge Distillation
Injoon Hwang, Haewon Park, Youngwan Lee +2
Low-rank adaption (LoRA) is a prominent method that adds a small number of learnable parameters to the frozen pre-trained weights for parameter-efficient fine-tuning. Prompted by t…