3 papers
cs.CV2026
Focusing Where Vision Matters: Selective Training for Large Vision Language Models via Visual Information Gain
Seulbi Lee, Sangheum Hwang
Large Vision Language Models (LVLMs) have achieved remarkable progress, yet they often suffer from language bias, producing answers without relying on visual evidence. While prior…
cs.CV2026
Localized Concept Erasure in Text-to-Image Diffusion Models via High-Level Representation Misdirection
Uichan Lee, Jeonghyeon Kim, Sangheum Hwang
Recent advances in text-to-image (T2I) diffusion models have seen rapid and widespread adoption. However, their powerful generative capabilities raise concerns about potential misu…
cs.CV2025
Reflexive Guidance: Improving OoDD in Vision-Language Models via Self-Guided Image-Adaptive Concept Generation
Jihyo Kim, Seulbi Lee, Sangheum Hwang
With the recent emergence of foundation models trained on internet-scale data and demonstrating remarkable generalization capabilities, such foundation models have become more wide…