5 papers
Multimodal UNcommonsense: From Odd to Ordinary and Ordinary to Odd
Yejin Son, Saejin Kim, Dongjun Min +1
Commonsense reasoning in multimodal contexts remains a foundational challenge in artificial intelligence. We introduce Multimodal UNcommonsense(MUN), a benchmark designed to evalua…
Zero-shot Multimodal Document Retrieval via Cross-modal Question Generation
Yejin Choi, Jaewoo Park, Janghan Yoon +3
Rapid advances in Multimodal Large Language Models (MLLMs) have expanded information retrieval beyond purely textual inputs, enabling retrieval from complex real world documents th…
Teaching Metric Distance to Discrete Autoregressive Language Models
Jiwan Chung, Saejin Kim, Yongrae Jo +3
Large language models (LLMs) operate as autoregressive predictors over discrete token vocabularies, a formulation that has enabled their adaptation far beyond natural language to v…
SEAL: Entangled White-box Watermarks on Low-Rank Adaptation
Giyeong Oh, Saejin Kim, Woohyun Cho +4
Recently, LoRA and its variants have become the de facto strategy for training and sharing task-specific versions of large pretrained models, thanks to their efficiency and simplic…
Scalp Diagnostic System With Label-Free Segmentation and Training-Free Image Translation
Youngmin Kim, Saejin Kim, Hoyeon Moon +2
Scalp disorders are highly prevalent worldwide, yet remain underdiagnosed due to limited access to expert evaluation and the high cost of annotation. Although AI-based approaches h…