I am a PhD student at Johns Hopkins University, advised by Bloomberg Distinguished Professor Dr. Alan Yuille.
I obtained my B.S. with summa cum laude honor from Rensselaer Polytechnic Institute in 2020 and I had a double major in Computer Science and Mathematics. During my undergraduate years, I had worked with Prof. Bülent Yener on discriminative and generative models for microstructure images and with Prof. Lirong Xia on preference learning from natural language.
I've spent time at Microsoft Research Asia, AWS CV Science, and Megvii Research as a research intern.
Email  / 
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Instagram
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Publications
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Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning
Zhuowan Li, Xingrui Wang, Elias Stengel-Eskin, Adam Kortylewski, Wufei Ma, Benjamin Van Durme, Alan Yuille
CVPR, 2023 (Highlight, 10% of accepted papers)
arXiv
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Code
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Summary
Visual Question Answering (VQA) models often perform poorly on out-of-distribution data and struggle on domain generalization. Due to the multi-modal nature of this task, multiple factors of variation are intertwined, making generalization difficult to analyze. This motivates us to introduce a virtual benchmark, Super-CLEVR, where different factors in VQA domain shifts can be isolated in order that their effects can be studied independently. Four factors are considered: visual complexity, question redundancy, concept distribution and concept compositionality. With controllably generated data, Super-CLEVR enables us to test VQA methods in situations where the test data differs from the training data along each of these axes. We study four existing methods, including two neural symbolic methods NSCL and NSVQA, and two non-symbolic methods FiLM and mDETR; and our proposed method, probabilistic NSVQA (P-NSVQA), which extends NSVQA with uncertainty reasoning. P-NSVQA outperforms other methods on three of the four domain shift factors. Our results suggest that disentangling reasoning and perception, combined with probabilistic uncertainty, form a strong VQA model that is more robust to domain shifts
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Robust Category-Level 6D Pose Estimation with Coarse-to-Fine Rendering of Neural Features
Wufei Ma, Angtian Wang, Alan Yuille, Adam Kortylewski
ECCV, 2022
arXiv
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Code
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Summary
We consider the problem of category-level 6D pose estimation from a single RGB image. Our approach represents an object category as a cuboid mesh and learns a generative model of the neural feature activations at each mesh vertex to perform pose estimation through differentiable rendering. A common problem of rendering-based approaches is that they rely on bounding box proposals, which do not convey information about the 3D rotation of the object and are not reliable when objects are partially occluded. Instead, we introduce a coarse-to-fine optimization strategy that utilizes the rendering process to estimate a sparse set of 6D object proposals, which are subsequently refined with gradient-based optimization.
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ROBIN: A Benchmark for Robustness to Individual Nuisances
Bingchen Zhao, Shaozuo Yu, Wufei Ma, Mingxin Yu, Shenxiao Mei, Angtian Wang, Ju He, Alan Yuille, Adam Kortylewski
ECCV, 2022 (Oral Presentation)
arXiv
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Summary
In this work, we introduce ROBIN, a benchmark dataset for diagnosing the robustness of vision algorithms to individual nuisances in real-world images. We provide results for a number of popular baselines and make several interesting observations. We believe our dataset provides a rich testbed to study the OOD robustness of vision algorithms and will help to significantly push forward research in this area.
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Deep Learning-Based Video Compression
Under Review
Research project at Microsoft Research Asia supervised by Dr. Bin Li and Dr. Jiahao Li.
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Making Group Decisions from Natural Language-Based Preferences
Farhad Moshin, Lei Luo, Wufei Ma, Inwon Kang, Zhibing Zhao, Ao Liu, Rohit Vaish, Lirong Xia
COMSOC, 2021
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COMSOC, 2021
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Summary
We propose a framework for making group decisions from natural language-based preferences. Experiments on the real world data confirms the efficacy of our method.
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Image-Driven Discriminative and Generative Machine Learning Algorithms for Establishing Microstructure-Processing Relationships
Wufei Ma, Elizabeth Kautz, Arun Baskaran, Aritra Chowdhury, Vineet Joshi, Bülent Yener, Daniel Lewis
Journal of Applied Physics, 2020
Project page
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PDF
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arXiv
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AIP
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Summary
Characterize 10 different microstructure representations with image texture features and quantitative metrics from image segmentation. For the microstructure generation task, two schemes are considered: 1) generating high-resolution (1024x1024) microstructure images from random noise; and 2) train a style transfer GAN for image generation conditioned on the segmentation label.
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An Image-Driven Machine Learning Approach to Kinetic Modeling of a Discontinuous Precipitation Reaction
Elizabeth Kautz*, Wufei Ma*, Saumyadeep Jana, Arun Devaraj, Vineet Joshi, Bülent Yener, Daniel Lewis
Materials Characterization, 2020
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Code
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arXiv
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ScienceDirect
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Summary
Kinetic modeling of a discontinuous precipitation reaction (5 phases) by 1) deep learning with CNN, and 2) performing image segmentation of various microstructure and quantizing the area fractions.
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The Adoption of Image-Driven Machine Learning for Microstructure Characterization and Materials Design: A Perspective
Arun Baskaran, Elizabeth Kautz, Aritra Chowdhary, Wufei Ma, Bülent Yener, Daniel Lewis
Preprint, 2021
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arXiv
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Summary
We first review the application of image-driven machine learning approaches to the field of materials characterization. Then we analyze and discuss the impact of various approaches at each step of the experiments.
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Academic Service
Reviewer
AROW @ ECCV 2022, Pre-training Workshop @ ICML 2022, NeurIPS 2022, CVPR 2022, ICLR 2022, CVPR 2023, ICML 2023
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Teaching
CS471/671 - NLP: Self-supervised Models Spring 2023
Graduate Course Assistant at Johns Hopkins University
CS182 - Foundations of Computer Science Fall 2021
Graduate Teaching Assistant at Purdue University
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Affiliations (current and previous)
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Copyright © 2017-21 Wufei Ma. Theme modified from Jon Barron's webpage.
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