Zhiheng Li

Pronunciation (🔊): Zhiheng: /ʤihɛnŋ/, Li: /li/

I am an Applied Scientist at Amazon AGI, working on multi-modal foundation models. I received my PhD degree in computer science at University of Rochester in 2023 (PhD advisor: Prof. Chenliang Xu) and my bachelor's degree at Wuhan University in 2018.

My research interests are computer vision, responsible AI (e.g., fairness, robustness, and interpretability), and generative AI (e.g., text-to-image).

Email: zhiheng.li -at- ieee.org

CV  /  Google Scholar  /  OpenReview  /  LinkedIn  /  Github

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clean-usnob FairRAG: Fair Human Generation via Fair Retrieval Augmentation
Robik Singh Shrestha, Yang Zou, Qiuyu Chen, Zhiheng Li, Yusheng Xie, Siqi Deng
CVPR, 2024
pdf / arxiv

We propose FairRAG to mitigate demographic biases of diffusion models (e.g., Stable Diffusion) based on Retrieval Augmented Generation (RAG).

clean-usnob Discover and Mitigate Multiple Biased Subgroups in Image Classifiers
Zeliang Zhang, Mingqian Feng, Zhiheng Li (Project Lead), Chenliang Xu
CVPR, 2024
pdf / arxiv / code

We proposed a new method to discover multiple biased subgroups based on Partial Least Squares (PLS), which enables dimension reduction guided by useful supervisions from the image classifier.

clean-usnob A Whac-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others
Zhiheng Li, Ivan Evtimov, Albert Gordo, Caner Hazirbas, Tal Hassner, Cristian Canton Ferrer, Chenliang Xu, Mark Ibrahim
CVPR, 2023
pdf / bibtex / arxiv / code / video

We introduce two new datasets (UrbanCars and ImageNet-W) to study multi-shortcut learning. Especially, ImageNet-W is created based on the newly found watermark shortcut in ImageNet affecting a broad range of vision models, including ResNet, RegNet, ViT, MoCov3, MAE, SEER, SWAG, model soups, and CLIP. Our work surfaces an overlooked challenge in shortcut learning: multi-shortcut mitigation resembles a Whac-A-Mole game, i.e., mitigating one shortcut amplifies others.

clean-usnob Discover and Mitigate Unknown Biases with Debiasing Alternate Networks
Zhiheng Li, Anthony Hoogs, Chenliang Xu
ECCV, 2022
pdf / bibtex / arxiv / code / video

We introduce Debiasing Alternate Networks (DebiAN) to discover and mitigate unknown biases of an image classifier. DebiAN trains two networks in an alternate fashion. The discoverer network identifies unknown biases in the classifier. The classifier mitigates biases found by the discoverer.

clean-usnob You Only Need a Good Embeddings Extractor to Fix Spurious Correlations
Raghav Mehta, Vítor Albiero, Li Chen, Ivan Evtimov, Tamar Glaser, Zhiheng Li, Tal Hassner
ECCV Responsible Computer Vision Workshop, 2022
pdf / bibtex / arxiv

Unlike existing approaches (e.g., GroupDRO) focusing on reweighting or rebalancing training data, we show that simply using embeddings from a large pretrained vision model extractor (e.g., SWAG) and training a linear classifier on top of it without training group information achieve state-of-the-art results in combating group shift on Waterbirds.

clean-usnob StyleT2I: Toward Compositional and High-Fidelity Text-to-Image Synthesis
Zhiheng Li, Martin Renqiang Min, Kai Li, Chenliang Xu
CVPR, 2022
pdf / bibtex / arxiv / code / video

We introduce a new framework, StyleT2I, to improve the compositionality of text-to-image synthesis, e.g., faithfully synthesizing a face image in “a man wearing lipstick” that is underrepresented in the training data (e.g., due to societal stereotypes).

clean-usnob Discover the Unknown Biased Attribute of an Image Classifier
Zhiheng Li, Chenliang Xu
ICCV, 2021
pdf / bibtex / arxiv / code / video

We study a new problem: discover the unknown bias (i.e., the bias that is out of human's conjecture) of an image classifier. We tackle it by optimizing a hyperplane in generative model's latent space. The semantic meaning of the bias can be interpreted from the variation in the synthesized traversal images based on the optimized latent hyperplane.

clean-usnob Deep Grouping Model for Unified Perceptual Parsing
Zhiheng Li, Wenxuan Bao, Jiayang Zheng, Chenliang Xu
CVPR, 2020
pdf / bibtex / arxiv

We propose DGM, which incorporates the traditional perceptual grouping process into modern CNN architecture for better contextual modeling, interpretability, and a lower computational overhead.

clean-usnob Learning a Weakly-Supervised Video Actor-Action Segmentation Model with a Wise Selection
Jie Chen, Zhiheng Li, Ross K Maddox, Jiebo Luo, Chenliang Xu
CVPR, 2020   (Oral Presentation)
pdf / bibtex / arxiv

A novel method to generate and evolve the pseudo-annotations for weakly-supervised video actor-action segmentation task.

clean-usnob Graph Neural Network Based Coarse-Grained Mapping Prediction
Zhiheng Li, Geemi P. Wellawatte, Maghesree Chakraborty, Heta A. Gandhi, Chenliang Xu, Andrew D. White
Chemical Science, 2020
pdf / bibtex / code / dataset / arxiv

We train a GNN to predict coarse-grained (CG) molecule, i.e., graph partitioning on the given molecule. We also collect HAM dataset, which provides CG mapping annotations of molecules.

clean-usnob Lip Movements Generation at a Glance
Lele Chen*, Zhiheng Li* (*Equal Contribution), Ross K Maddox, Zhiyao Duan, Chenliang Xu
ECCV, 2018
pdf / code / video / bibtex / arxiv

Given an audio speech and a lip image of an arbitrary target identity, synthesize lip movements of the target identity saying the speech.

Amazon AGI

07/2023 - Present

Bellevue, WA

Applied Scientist
Meta AI

05/2022 - 12/2022

Seattle, WA

Research Intern

Mentors: Ivan Evtimov, Mark Ibrahim, Albert Gordo
NEC Labs America

06/2021 - 09/2021

Princeton, NJ

Research Intern

Mentors: Martin Renqiang Min, Kai Li
University of Rochester

08/2018 - 07/2023

Rochester, NY

Ph.D. in Computer Science

Advisor: Chenliang Xu
Wuhan University

09/2014 - 06/2018

Wuhan, Hubei, China

B.Eng. in Software Engineering
Conference Reviewer: NeurIPS'20, CVPR'21, ICML'21, ICCV'21, NeurIPS'21, ICLR'22, AAAI'22, CVPR'22, ICML'22, ECCV'22, NeurIPS'22 (top reviewer), CVPR'23, ICML'23, FAccT'23, ICCV'23, NeurIPS'23, ICLR'24, CVPR'24, ICML'24, ECCV'24
Journal Reviewer: TMLR, TPAMI, TMM
Volunteer: FAccT'21, ICLR'21

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