Matthew Gwilliam

I am a third year Ph.D student (started Fall 2020) in the department of Computer Science at the University of Maryland (UMD), advised by Professor Abhinav Shrivastava. I am studying computer vision.

I completed my B.S. in Computer Science at Brigham Young University in 2019. During undergrad I worked part-time at Qualtrics, and after graduation I worked there full-time before I started my Ph.D.

While at BYU, I was fortunate to work with Ryan Farrell, who have helped me grow as a researcher and a person and ultimately decide to pursue my graduate degree.

Email / CV / Google Scholar

Research

I am interested in computer vision models that learn without labels. More specifically, I am interested in methods that can learn universal image representations in an unsupervised manner. Currently, that work focuses on models based on diffusion and implicit neural representation (but mostly on INR). I am working with the sorts of tasks that these models are useful for: video retrieval, compression, generation; image classification, clustering, etc.

	Diffusion Models Beat GANs on Image Classification Matthew Gwilliam, Soumik Mukhopadhyay, Vatsal Agarwal Namitha Padmanabhan Archana Swaminathan Tianyi Zhou, Abhinav Shrivastava Under Review Project Page \|Paper Explore diffusion models as unified unsupervised image representation learning models.
	HNeRV: A Hybrid Neural Representation for Videos Hao Chen, Matthew Gwilliam, Ser-Nam Lim, Abhinav Shrivastava IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023 Project Page \| Paper \| Code Combine the strengths of implicit (NeRV) and explicit (autoencoder) representation to create a hybrid neural representation for video with good properties for representation, compression, and editing.
	CNeRV: Content-adaptive Neural Representation for Visual Data Hao Chen, Matthew Gwilliam, Bo He, Ser-Nam Lim, Abhinav Shrivastava British Machine Vision Conference (BMVC), 2022 (ORAL) Project Page \| Paper Make implicit video representation networks generalize to unseen data by swapping time embedding for content-aware embedding that is computed as a unique summary of each frame.
	Beyond Supervised vs. Unsupervised: Representative Benchmarking and Analysis of Image Representation Learning Matthew Gwilliam, Abhinav Shrivastava IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022 Project Page \| Paper \| Code Examine, compare, and contrast popular unsupervised image representation learning methods, showing that there are significant differences based on specific algorithm used, and "supervised vs. unsupervised" comparisons which neglect these differences tend to over-generalize.
	Rethinking Common Assumptions to Mitigate Racial Bias in Face Recognition Datasets Matthew Gwilliam, Srinidhi Hegde Lade Tinubu Alex Hanson IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) , 2021 Paper \| Code Reveal the role of data in racial bias for facial recognition systems, and the flaws underlying the assumption that balanced data results in fair performance.
	Fair Comparison: Quantifying Variance in Results for Fine-grained Visual Categorization Matthew Gwilliam, Adam Teuscher Connor Anderson Ryan Farrell IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , 2021 Paper Uncover the large, often-ignored amount of variance in FGVC systems across training runs, on the dataset level, but more particularly in terms of the classification performance for individual classes.
	Intelligent Image Collection: Building the Optimal Dataset Matthew Gwilliam, Ryan Farrell IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , 2020 Paper Propose smart practices to optimize image curation, such that classification accuracy is maximized for a given constrained dataset size.

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