6 papers
Keep It CALM: Analyzing the Limits of Global Unsafety in Text-to-Image Generation
NaHyeon Park, Minhyun Lee, Hyunjung Shim
Training-free safeguards for text-to-image generation often rely on a reusable safety signal, such as an unsafe direction or global toxic subspace, applied broadly across prompts.…
Blind to Position, Biased in Language: Probing Mid-Layer Representational Bias in Vision-Language Encoders for Zero-Shot Language-Grounded Spatial Understanding
Na Min An, Inha Kang, Minhyun Lee +1
Vision-Language Encoders (VLEs) are widely adopted as the backbone of zero-shot referring image segmentation (RIS), enabling text-guided localization without task-specific training…
Fine-Grained Image-Text Correspondence with Cost Aggregation for Open-Vocabulary Part Segmentation
Jiho Choi, Seonho Lee, Minhyun Lee +2
Open-Vocabulary Part Segmentation (OVPS) is an emerging field for recognizing fine-grained parts in unseen categories. We identify two primary challenges in OVPS: (1) the difficult…
MaskRIS: Semantic Distortion-aware Data Augmentation for Referring Image Segmentation
Minhyun Lee, Seungho Lee, Song Park +3
Referring Image Segmentation (RIS) is an advanced vision-language task that involves identifying and segmenting objects within an image as described by free-form text descriptions.…
Understanding Multi-Granularity for Open-Vocabulary Part Segmentation
Jiho Choi, Seonho Lee, Seungho Lee +2
Open-vocabulary part segmentation (OVPS) is an emerging research area focused on segmenting fine-grained entities using diverse and previously unseen vocabularies. Our study highli…
SeiT++: Masked Token Modeling Improves Storage-efficient Training
Minhyun Lee, Song Park, Byeongho Heo +2
Recent advancements in Deep Neural Network (DNN) models have significantly improved performance across computer vision tasks. However, achieving highly generalizable and high-perfo…