Sheng Cheng

I'm a PhD student at ASU. I'm working with Yezhou Yang, and co-advised by Yi Ren. I closely collaborate with Maitreya Patel, Changhoon Kim. Previously, I received my M.Eng. in Electrical Engineering from University of Illinois at Urbana-Champaign, working with Ruoyu Sun, and received B.S. from Huazhong University of Science and Technology.

Email / CV / Scholar / Twitter / Github / Linkedin

Research

I'm interested in computer vision and machine learning. My focus lies in the domain of vision & language (particularly in Text-to-Image generation) and domain generalization & robustness.

	Revising Text-to-Image Prior for Improved Text Conditioned Image Generations Maitreya Patel, Changhoon Kim, Sheng Cheng, Chitta Baral, Yezhou Yang CVPR, 2024 project page / demo / arXiv / code / media coverage (Twitter of AK, MarkTechPost, MultiPlatformAI, Video discussion, Paper Digest) Improving the Parameter and Data Efficiency of the Text-to-Image Priors for UnCLIP Family Models with contrastive loss.
	WOUAF: Weight Modulation for User Attribution and Fingerprinting in Text-to-Image Diffusion Models Changhoon Kim, Kyle Min, Maitreya Patel, Sheng Cheng, Yezhou Yang CVPR, 2024 project page / demo / arXiv Enabling the Integration of up to 32-bit (~4 billion) fingerprints into Text-to-Image Diffusion Models without loss in image quality.
	Self-supervised Learning to Discover Physical Objects and Predict Their Interactions from Raw Videos Sheng Cheng, Yezhou Yang, Yang Jiao, Yi Ren NeurIPS AI4Science workshop, 2023 arXiv Jointly learning to discover physical objects and predict their dynamics in the videos for physical environment.
	Adversarial Bayesian Augmentation for Single-Source Domain Generalization Sheng Cheng, Tejas Gokhale, Yezhou Yang ICCV, 2023 arXiv / code Adversarial Learning + Bayesian neural network for single-source domain generalization.
	SSR-GNNs: Stroke-based Sketch Representation with Graph Neural Networks Sheng Cheng, Yi Ren, Yezhou Yang CVPR Sketch workshop, 2022 arXiv / code Transformation invariant sketch recognition by decomposing to strokes and composing by graph neural network.
	Data-Driven Learning of Three-Point Correlation Functions as Microstructure Representations Sheng Cheng, Yang Jiao, Yi Ren Acta Materialia, 2022 arXiv / code Learning the microstructure representation by 3-point correlation functions.
	Evaluating the Robustness of Bayesian Neural Networks Against Different Types of Attacks Yutian Pang, Sheng Cheng, Jueming Hu, Yongming Liu, CVPR Adversarial Machine Learning workshop, 2021 arXiv Evaluating the robustness gain of Bayesian neural networks on image classification tasks.