Intelligent Image Collection: Building the Optimal Dataset. - Matthew Gwilliam; Ryan Farrell (WACV, 2020)

Facing the Hard Problems in FGVC. - Connor Anderson 001; Matthew Gwilliam; Adam Teuscher; Andrew Merrill; Ryan Farrell (CoRR, 2020)

Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation. - Eva Vanmassenhove; Dimitar Sht. Shterionov; Matthew Gwilliam (EACL, 2021)

Rethinking Common Assumptions to Mitigate Racial Bias in Face Recognition Datasets. - Matthew Gwilliam; Srinidhi Hegde; Lade Tinubu; Alex Hanson 002 (ICCVW, 2021)

Fair Comparison: Quantifying Variance in Results for Fine-grained Visual Categorization. - Matthew Gwilliam; Adam Teuscher; Connor Anderson 001; Ryan Farrell (WACV, 2021)

Fair Comparison: Quantifying Variance in Resultsfor Fine-grained Visual Categorization. - Matthew Gwilliam; Adam Teuscher; Connor Anderson 001; Ryan Farrell (CoRR, 2021)

CNeRV: Content-adaptive Neural Representation for Visual Data. - Hao Chen 066; Matthew Gwilliam; Bo He 004; Ser-Nam Lim; Abhinav Shrivastava (BMVC, 2022)

Beyond Supervised vs. Unsupervised: Representative Benchmarking and Analysis of Image Representation Learning. - Matthew Gwilliam; Abhinav Shrivastava (CVPR, 2022)

HNeRV: A Hybrid Neural Representation for Videos. - Hao Chen 066; Matthew Gwilliam; Ser-Nam Lim; Abhinav Shrivastava (CVPR, 2023)

Diffusion Models Beat GANs on Image Classification. - Soumik Mukhopadhyay 001; Matthew Gwilliam; Vatsal Agarwal; Namitha Padmanabhan; Archana Swaminathan; Srinidhi Hegde; Tianyi Zhou 001; Abhinav Shrivastava (CoRR, 2023)

Do text-free diffusion models learn discriminative visual representations? - Soumik Mukhopadhyay 001; Matthew Gwilliam; Yosuke Yamaguchi; Vatsal Agarwal; Namitha Padmanabhan; Archana Swaminathan; Tianyi Zhou 001; Abhinav Shrivastava (CoRR, 2023)

Explaining the Implicit Neural Canvas: Connecting Pixels to Neurons by Tracing Their Contributions. - Namitha Padmanabhan; Matthew Gwilliam; Pulkit Kumar; Shishira R. Maiya; Max Ehrlich; Abhinav Shrivastava (CVPR, 2024)

Latent-INR: A Flexible Framework for Implicit Representations of Videos with Discriminative Semantics. - Shishira R. Maiya; Anubhav Gupta; Matthew Gwilliam; Max Ehrlich; Abhinav Shrivastava (ECCV (15), 2024)

Do Text-Free Diffusion Models Learn Discriminative Visual Representations? - Soumik Mukhopadhyay 001; Matthew Gwilliam; Yosuke Yamaguchi; Vatsal Agarwal; Namitha Padmanabhan; Archana Swaminathan; Tianyi Zhou 001; Jun Ohya; Abhinav Shrivastava (ECCV (60), 2024)

Quantifying NBA Shot Quality: A Deep Network Approach. - Archit Ramanasai Kambhamettu; Abhinav Shrivastava; Matthew Gwilliam (MMSports@MM, 2024)

Elusive Images: Beyond Coarse Analysis for Fine-Grained Recognition. - Connor Anderson 001; Matthew Gwilliam; Evelyn Gaskin; Ryan Farrell (WACV, 2024)

NeRF-Aug: Data Augmentation for Robotics with Neural Radiance Fields. - Eric Zhu; Mara Levy; Matthew Gwilliam; Abhinav Shrivastava (CoRR, 2024)

A Video is Worth 10, 000 Words: Training and Benchmarking with Diverse Captions for Better Long Video Retrieval. - Matthew Gwilliam; Michael Cogswell; Meng Ye 002; Karan Sikka; Abhinav Shrivastava; Ajay Divakaran (WACV, 2025)

Accelerate High-Quality Diffusion Models with Inner Loop Feedback. - Matthew Gwilliam; Han Cai; Di Wu; Abhinav Shrivastava; Zhiyu Cheng (CoRR, 2025)

Utilization of Neighbor Information for Image Classification with Different Levels of Supervision. - Gihan Jayatilaka; Abhinav Shrivastava; Matthew Gwilliam (CoRR, 2025)

Evolutionary Caching to Accelerate Your Off-the-Shelf Diffusion Model. - Anirud Aggarwal; Abhinav Shrivastava; Matthew Gwilliam (CoRR, 2025)

How to Design and Train Your Implicit Neural Representation for Video Compression. - Matthew Gwilliam; Roy Zhang; Namitha Padmanabhan; Hongyang Du; Abhinav Shrivastava (CoRR, 2025)

Towards Multimodal Understanding via Stable Diffusion as a Task-Aware Feature Extractor. - Vatsal Agarwal; Matthew Gwilliam; Gefen Kohavi; Eshan Verma; Daniel Ulbricht; Abhinav Shrivastava (CoRR, 2025)