Matthew Gwilliam

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 19

Top Topics

Visual Representations

Simplifying Assumptions

Top Venues

Publications

Connor Anderson, Matthew Gwilliam, Evelyn Gaskin, Ryan Farrell
Elusive Images: Beyond Coarse Analysis for Fine-Grained Recognition. WACV (2024)
Namitha Padmanabhan, Matthew Gwilliam, Pulkit Kumar, Shishira R. Maiya, Max Ehrlich, Abhinav Shrivastava
Explaining the Implicit Neural Canvas: Connecting Pixels to Neurons by Tracing their Contributions. CoRR (2024)
Soumik Mukhopadhyay, Matthew Gwilliam, Yosuke Yamaguchi, Vatsal Agarwal, Namitha Padmanabhan, Archana Swaminathan, Tianyi Zhou, Abhinav Shrivastava
Do text-free diffusion models learn discriminative visual representations? CoRR (2023)
Matthew Gwilliam, Michael Cogswell, Meng Ye, Karan Sikka, Abhinav Shrivastava, Ajay Divakaran
A Video is Worth 10, 000 Words: Training and Benchmarking with Diverse Captions for Better Long Video Retrieval. CoRR (2023)
Hao Chen, Matthew Gwilliam, Ser-Nam Lim, Abhinav Shrivastava
HNeRV: A Hybrid Neural Representation for Videos. CVPR (2023)
Soumik Mukhopadhyay, Matthew Gwilliam, Vatsal Agarwal, Namitha Padmanabhan, Archana Swaminathan, Srinidhi Hegde, Tianyi Zhou, Abhinav Shrivastava
Diffusion Models Beat GANs on Image Classification. CoRR (2023)
Hao Chen, Matthew Gwilliam, Ser-Nam Lim, Abhinav Shrivastava
HNeRV: A Hybrid Neural Representation for Videos. CoRR (2023)
Hao Chen, Matthew Gwilliam, Bo He, Ser-Nam Lim, Abhinav Shrivastava
CNeRV: Content-adaptive Neural Representation for Visual Data. BMVC (2022)
Hao Chen, Matthew Gwilliam, Bo He, Ser-Nam Lim, Abhinav Shrivastava
CNeRV: Content-adaptive Neural Representation for Visual Data. CoRR (2022)
Matthew Gwilliam, Abhinav Shrivastava
Beyond Supervised vs. Unsupervised: Representative Benchmarking and Analysis of Image Representation Learning. CVPR (2022)
Matthew Gwilliam, Abhinav Shrivastava
Beyond Supervised vs. Unsupervised: Representative Benchmarking and Analysis of Image Representation Learning. CoRR (2022)
Eva Vanmassenhove, Dimitar Sht. Shterionov, Matthew Gwilliam
Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation. EACL (2021)
Matthew Gwilliam, Srinidhi Hegde, Lade Tinubu, Alex Hanson
Rethinking Common Assumptions to Mitigate Racial Bias in Face Recognition Datasets. CoRR (2021)
Matthew Gwilliam, Srinidhi Hegde, Lade Tinubu, Alex Hanson
Rethinking Common Assumptions to Mitigate Racial Bias in Face Recognition Datasets. ICCVW (2021)
Matthew Gwilliam, Adam Teuscher, Connor Anderson, Ryan Farrell
Fair Comparison: Quantifying Variance in Results for Fine-grained Visual Categorization. WACV (2021)
Eva Vanmassenhove, Dimitar Sht. Shterionov, Matthew Gwilliam
Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation. CoRR (2021)
Matthew Gwilliam, Adam Teuscher, Connor Anderson, Ryan Farrell
Fair Comparison: Quantifying Variance in Resultsfor Fine-grained Visual Categorization. CoRR (2021)
Connor Anderson, Matthew Gwilliam, Adam Teuscher, Andrew Merrill, Ryan Farrell
Facing the Hard Problems in FGVC. CoRR (2020)
Matthew Gwilliam, Ryan Farrell
Intelligent Image Collection: Building the Optimal Dataset. WACV (2020)