4 papers
Focus, Don't Prune: Identifying Instruction-Relevant Regions for Information-Rich Image Understanding
Mincheol Kwon, Minseung Lee, Seonga Choi +7
Large Vision-Language Models (LVLMs) have shown strong performance across various multimodal tasks by leveraging the reasoning capabilities of Large Language Models (LLMs). However…
MambaEye: A Size-Agnostic Visual Encoder with Causal Sequential Processing
Changho Choi, Minho Kim, Jinkyu Kim
Despite decades of progress, a truly input-size agnostic visual encoder-a fundamental characteristic of human vision-has remained elusive. We address this limitation by proposing \…
Watermarking for Factuality: Guiding Vision-Language Models Toward Truth via Tri-layer Contrastive Decoding
Kyungryul Back, Seongbeom Park, Milim Kim +6
Large Vision-Language Models (LVLMs) have recently shown promising results on various multimodal tasks, even achieving human-comparable performance in certain cases. Nevertheless,…
Mitigating the Linguistic Gap with Phonemic Representations for Robust Cross-lingual Transfer
Haeji Jung, Changdae Oh, Jooeon Kang +4
Approaches to improving multilingual language understanding often struggle with significant performance gaps between high-resource and low-resource languages. While there are effor…