3 papers
cs.CV2026
ERGO: Efficient High-Resolution Visual Understanding for Vision-Language Models
Jewon Lee, Wooksu Shin, Seungmin Yang +5
Efficient processing of high-resolution images is crucial for real-world vision-language applications. However, existing Large Vision-Language Models (LVLMs) incur substantial comp…
cs.CV2025
Efficient LLaMA-3.2-Vision by Trimming Cross-attended Visual Features
Jewon Lee, Ki-Ung Song, Seungmin Yang +6
Visual token reduction lowers inference costs caused by extensive image features in large vision-language models (LVLMs). Unlike relevant studies that prune tokens in self-attentio…
cs.CL2024
Assessing the Answerability of Queries in Retrieval-Augmented Code Generation
Geonmin Kim, Jaeyeon Kim, Hancheol Park +2
Thanks to unprecedented language understanding and generation capabilities of large language model (LLM), Retrieval-augmented Code Generation (RaCG) has recently been widely utiliz…