6 papers
SAVE: Sparse Autoencoder-Driven Visual Information Enhancement for Mitigating Object Hallucination
Sangha Park, Seungryong Yoo, Jisoo Mok +1
Although Multimodal Large Language Models (MLLMs) have advanced substantially, they remain vulnerable to object hallucination caused by language priors and visual information loss.…
Guiding What Not to Generate: Automated Negative Prompting for Text-Image Alignment
Sangha Park, Eunji Kim, Yeongtak Oh +2
Despite substantial progress in text-to-image generation, achieving precise text-image alignment remains challenging, particularly for prompts with rich compositional structure or…
DefectFill: Realistic Defect Generation with Inpainting Diffusion Model for Visual Inspection
Jaewoo Song, Daemin Park, Kanghyun Baek +4
Developing effective visual inspection models remains challenging due to the scarcity of defect data. While image generation models have been used to synthesize defect images, prod…
Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models
Mingi Jung, Saehyung Lee, Eunji Kim +1
Detailed image captioning is essential for tasks like data generation and aiding visually impaired individuals. High-quality captions require a balance between precision and recall…
Superpixel Tokenization for Vision Transformers: Preserving Semantic Integrity in Visual Tokens
Jaihyun Lew, Soohyuk Jang, Jaehoon Lee +6
Transformers, a groundbreaking architecture proposed for Natural Language Processing (NLP), have also achieved remarkable success in Computer Vision. A cornerstone of their success…
Interpretable Next-token Prediction via the Generalized Induction Head
Eunji Kim, Sriya Mantena, Weiwei Yang +3
While large transformer models excel in predictive performance, their lack of interpretability restricts their usefulness in high-stakes domains. To remedy this, we propose the Gen…