Yihong Sun's Homepage

About Me

I am a third-year CS PhD Student at Cornell University, advised by Prof. Bharath Hariharan. My work is supported by NSF GRFP and my research interests are computer vision and machine learning, especially in building visual perception systems that can learn from minimal supervision.

Previously, I obtained my Bachelor's degree from Johns Hopkins University where I studied Computer Science, Neuroscience, Applied Mathematics & Statistics, and Cognitive Science. During my undergradute studies, I worked with Bloomberg Distinguished Prof. Alan Yuille and Dr. Adam Kortylewski.

If you would like to chat with me, please drop me an email!

Publications

	MOD-UV: Learning Mobile Object Detectors from Unlabeled Videos Yihong Sun, Bharath Hariharan ECCV 2024 Paper / Code / Poster / Project Page We build an unsupervised mobile object detector from unlabeled videos only by leveraging independent motion information.
	Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes Yihong Sun, Bharath Hariharan NeurIPS 2023 Paper / Code / Poster / Project Page We improve unsupervised monocular depth estimation for dynamical scenes by modeling 3D independent flow and motion segmentation.
	Amodal Segmentation through Out-of-Task and Out-of-Distribution Generalization with a Bayesian Model Yihong Sun, Adam Kortylewski, Alan Yuille CVPR 2022 Paper / Code / Poster / Supplementary We estimate amodal segmentation using a Bayesian generative model trained from non-occluded images and box-level annotations only.
	Robust Instance Segmentation through Reasoning about Multi-Object Occlusion Xiaoding Yuan, Adam Kortylewski, Yihong Sun, Alan Yuille CVPR 2021 Paper / Code We reason about multi-object self-occlusions by inspecting part-level activations of a Bayesian generative model.
	Robust Object Detection Under Occlusion With Context-Aware CompositionalNets Angtian Wang(), Yihong Sun()*, Adam Kortylewski, Alan Yuille CVPR 2020* Paper / Video / Data (*) indicates joint first authors We improve object detection under partial occlusion by regulating contextual bias and enhancing localization via compositional part voting.
	Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition under Occlusion Adam Kortylewski, Qing Liu, Angtian Wang, Yihong Sun, Alan Yuille IJCV 2020 Paper / Code We propose CompositionalNets, interpretable deep architectures with innate robustness to partial occlusion, for image classification and object detection.

Experience

Student Researcher

May 2024 - Now

- Hosts: Ting Liu, Liangzhe Yuan, Jennifer Sun, Long Zhao, Hao Zhou.

Teaching

	Graduate Teaching Assistant Bowers CIS College of Computing and Information Science - CS4670/5670 Introduction to Computer Vision (SP23) - CS4787/5777 Principles of Large-Scale Machine Learning (FA22)
	Undergraduate Course Assistant Department of Computer Science - EN.601.783 Vision as Bayesian Inference (SP22) - AS.050.375/675 Probabilistic Models of the Visual Cortex (FA21, FA20) - EN.601.226 Data Structures (SP21, FA20, SP20)