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cs.CV2026
UniSAFE: A Comprehensive Benchmark for Safety Evaluation of Unified Multimodal Models
Segyu Lee, Boryeong Cho, Hojung Jung +8
Unified Multimodal Models (UMMs) offer powerful cross-modality capabilities but introduce new safety risks not observed in single-task models. Despite their emergence, existing saf…
cs.CV2023★ 1 cited
Leveraging Normalization Layer in Adapters With Progressive Learning and Adaptive Distillation for Cross-Domain Few-Shot Learning
Yongjin Yang, Taehyeon Kim, Se-Young Yun
Cross-domain few-shot learning presents a formidable challenge, as models must be trained on base classes and then tested on novel classes from various domains with only a few samp…
cs.CV2023
Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language Models
Yongjin Yang, Jongwoo Ko, Se-Young Yun
Vision-language models (VLMs) like CLIP have demonstrated remarkable applicability across a variety of downstream tasks, including zero-shot image classification. Recently, the use…