272 citations · 338 across the 14 of their papers we have counts for
14 papers
RMLer: Synthesizing Novel Objects across Diverse Categories via Reinforcement Mixing Learning
Jun Li, Zikun Chen, Haibo Chen +2
Novel object synthesis by integrating distinct textual concepts from diverse categories remains a significant challenge in Text-to-Image (T2I) generation. Existing methods often su…
VMDiff: Visual Mixing Diffusion for Limitless Cross-Object Synthesis
Zeren Xiong, Yue Yu, Zedong Zhang +3
Creating novel images by fusing visual cues from multiple sources is a fundamental yet underexplored problem in image-to-image generation, with broad applications in artistic creat…
Learning Generalized Residual Exchange-Correlation-Uncertain Functional for Density Functional Theory
Sizhuo Jin, Shuo Chen, Jianjun Qian +2
Density Functional Theory (DFT) stands as a widely used and efficient approach for addressing the many-electron Schrödinger equation across various domains such as physics, chemist…
PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
Zheng Li, Xiang Li, Xinyi Fu +4
Prompt learning has emerged as a valuable technique in enhancing vision-language models (VLMs) such as CLIP for downstream tasks in specific domains. Existing work mainly focuses o…
Creative Birds: Self-Supervised Single-View 3D Style Transfer
Renke Wang, Guimin Que, Shuo Chen +3
In this paper, we propose a novel method for single-view 3D style transfer that generates a unique 3D object with both shape and texture transfer. Our focus lies primarily on birds…
Distribution Shift Matters for Knowledge Distillation with Webly Collected Images
Jialiang Tang, Shuo Chen, Gang Niu +2
Knowledge distillation aims to learn a lightweight student network from a pre-trained teacher network. In practice, existing knowledge distillation methods are usually infeasible w…