982 citations · 2.8k across the 131 of their papers we have counts for
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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…
Collaborative Score Distillation for Consistent Visual Synthesis
Subin Kim, Kyungmin Lee, June Suk Choi +3
Generative priors of large-scale text-to-image diffusion models enable a wide range of new generation and editing applications on diverse visual modalities. However, when adapting…
S-CLIP: Semi-supervised Vision-Language Learning using Few Specialist Captions
Sangwoo Mo, Minkyu Kim, Kyungmin Lee +1
Vision-language models, such as contrastive language-image pre-training (CLIP), have demonstrated impressive results in natural image domains. However, these models often struggle…
Video Probabilistic Diffusion Models in Projected Latent Space
Sihyun Yu, Kihyuk Sohn, Subin Kim +1
Despite the remarkable progress in deep generative models, synthesizing high-resolution and temporally coherent videos still remains a challenge due to their high-dimensionality an…
IFSeg: Image-free Semantic Segmentation via Vision-Language Model
Sukmin Yun, Seong Hyeon Park, Paul Hongsuck Seo +1
Vision-language (VL) pre-training has recently gained much attention for its transferability and flexibility in novel concepts (e.g., cross-modality transfer) across various visual…
OAMixer: Object-aware Mixing Layer for Vision Transformers
Hyunwoo Kang, Sangwoo Mo, Jinwoo Shin
Patch-based models, e.g., Vision Transformers (ViTs) and Mixers, have shown impressive results on various visual recognition tasks, alternating classic convolutional networks. Whil…