4.8k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Peking UniversityCN85 papers
- Microsoft (United States)US82 papers
- Tsinghua UniversityCN81 papers
- University of Science and Technology of ChinaCN81 papers
- Hong Kong University of Science and TechnologyHK40 papers
- Beihang UniversityCN36 papers
- Chinese Academy of SciencesCN36 papers
- Shanghai Jiao Tong UniversityCN33 papers
- Microsoft Research (United Kingdom)GB28 papers
- Zhejiang UniversityCN28 papers
- Fudan UniversityCN24 papers
- Carnegie Mellon UniversityUS19 papers
6 papers · 2 filters
Embedding an Ethical Mind: Aligning Text-to-Image Synthesis via Lightweight Value Optimization
Xingqi Wang, Xiaoyuan Yi, Xing Xie +1
Recent advancements in diffusion models trained on large-scale data have enabled the generation of indistinguishable human-level images, yet they often produce harmful content misa…
Neural-Polyptych: Content Controllable Painting Recreation for Diverse Genres
Yiming Zhao, Dewen Guo, Zhouhui Lian +6
To bridge the gap between artists and non-specialists, we present a unified framework, Neural-Polyptych, to facilitate the creation of expansive, high-resolution paintings by seaml…
Clustering-friendly Representation Learning for Enhancing Salient Features
Toshiyuki Oshima, Kentaro Takagi, Kouta Nakata
Recently, representation learning with contrastive learning algorithms has been successfully applied to challenging unlabeled datasets. However, these methods are unable to disting…
Exploiting Diffusion Prior for Out-of-Distribution Detection
Armando Zhu, Jiabei Liu, Keqin Li +4
Out-of-distribution (OOD) detection is crucial for deploying robust machine learning models, especially in areas where security is critical. However, traditional OOD detection meth…
DiLightNet: Fine-grained Lighting Control for Diffusion-based Image Generation
Chong Zeng, Yue Dong, Pieter Peers +3
This paper presents a novel method for exerting fine-grained lighting control during text-driven diffusion-based image generation. While existing diffusion models already have the…
CCA: Collaborative Competitive Agents for Image Editing
Tiankai Hang, Shuyang Gu, Dong Chen +2
This paper presents a novel generative model, Collaborative Competitive Agents (CCA), which leverages the capabilities of multiple Large Language Models (LLMs) based agents to exec…