5 citations · 11 across the 6 of their papers we have counts for
6 papers
Safety Alignment Backfires: Preventing the Re-emergence of Suppressed Concepts in Fine-tuned Text-to-Image Diffusion Models
Sanghyun Kim, Moonseok Choi, Jinwoo Shin +1
Fine-tuning text-to-image diffusion models is widely used for personalization and adaptation for new domains. In this paper, we identify a critical vulnerability of fine-tuning: sa…
Safeguard Text-to-Image Diffusion Models with Human Feedback Inversion
Sanghyun Kim, Seohyeon Jung, Balhae Kim +3
This paper addresses the societal concerns arising from large-scale text-to-image diffusion models for generating potentially harmful or copyrighted content. Existing models rely h…
Slot-Mixup with Subsampling: A Simple Regularization for WSI Classification
Seongho Keum, Sanghyun Kim, Soojeong Lee +1
Whole slide image (WSI) classification requires repetitive zoom-in and out for pathologists, as only small portions of the slide may be relevant to detecting cancer. Due to the lac…
Towards Safe Self-Distillation of Internet-Scale Text-to-Image Diffusion Models
Sanghyun Kim, Seohyeon Jung, Balhae Kim +3
Large-scale image generation models, with impressive quality made possible by the vast amount of data available on the Internet, raise social concerns that these models may generat…
Relational Context Learning for Human-Object Interaction Detection
Sanghyun Kim, Deunsol Jung, Minsu Cho
Recent state-of-the-art methods for HOI detection typically build on transformer architectures with two decoder branches, one for human-object pair detection and the other for inte…
Devil's on the Edges: Selective Quad Attention for Scene Graph Generation
Deunsol Jung, Sanghyun Kim, Won Hwa Kim +1
Scene graph generation aims to construct a semantic graph structure from an image such that its nodes and edges respectively represent objects and their relationships. One of the m…