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!
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
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Yihong Sun,
Bharath Hariharan
ECCV 2024
We build an unsupervised mobile object detector from unlabeled videos only by leveraging independent motion information.
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Yihong Sun,
Bharath Hariharan
NeurIPS 2023
We improve unsupervised monocular depth estimation for dynamical scenes by modeling 3D independent flow and motion segmentation.
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Yihong Sun,
Adam Kortylewski,
Alan Yuille
CVPR 2022
We tackle amodal segmentation using a Bayesian generative model trained from non-occluded images and box-level annotations only.
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CVPR 2021
We reason about multi-object self-occlusions by inspecting part-level activations of a Bayesian generative model.
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CVPR 2020
(*) indicates joint first authors
We improve object detection under partial occlusion by regulating contextual bias and enhancing localization via compositional part voting.
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IJCV 2020
We propose CompositionalNets, interpretable deep architectures with innate robustness to partial occlusion, for image classification and object detection.
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Experience
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May 2024 - Now
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Teaching
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Bowers CIS College of Computing and Information Science
- CS4670/5670 Introduction to Computer Vision (SP23)
- CS4787/5777 Principles of Large-Scale Machine Learning (FA22)
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Department of Computer Science
- EN.601.783 Vision as Bayesian Inference (SP22)
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