Matthew Gwilliam

According to our database, Matthew Gwilliam authored at least 14 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

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

CoRR, 2024

Elusive Images: Beyond Coarse Analysis for Fine-Grained Recognition.

Connor Anderson, Matthew Gwilliam, Evelyn Gaskin, Ryan Farrell

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

2023

A Video is Worth 10, 000 Words: Training and Benchmarking with Diverse Captions for Better Long Video Retrieval.

Matthew Gwilliam, Michael Cogswell, Meng Ye, Karan Sikka, Abhinav Shrivastava, Ajay Divakaran

CoRR, 2023

Do text-free diffusion models learn discriminative visual representations?

Soumik Mukhopadhyay, Matthew Gwilliam, Yosuke Yamaguchi, Vatsal Agarwal, Namitha Padmanabhan, Archana Swaminathan, Tianyi Zhou, Abhinav Shrivastava

CoRR, 2023

Diffusion Models Beat GANs on Image Classification.

Soumik Mukhopadhyay, Matthew Gwilliam, Vatsal Agarwal, Namitha Padmanabhan, Archana Swaminathan, Srinidhi Hegde, Tianyi Zhou, Abhinav Shrivastava

CoRR, 2023

HNeRV: A Hybrid Neural Representation for Videos.

Hao Chen, Matthew Gwilliam, Ser-Nam Lim, Abhinav Shrivastava

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Beyond Supervised vs. Unsupervised: Representative Benchmarking and Analysis of Image Representation Learning.

Matthew Gwilliam, Abhinav Shrivastava

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

CNeRV: Content-adaptive Neural Representation for Visual Data.

Hao Chen, Matthew Gwilliam, Bo He, Ser-Nam Lim, Abhinav Shrivastava

Proceedings of the 33rd British Machine Vision Conference 2022, 2022

2021

Fair Comparison: Quantifying Variance in Resultsfor Fine-grained Visual Categorization.

Matthew Gwilliam, Adam Teuscher, Connor Anderson, Ryan Farrell

CoRR, 2021

Fair Comparison: Quantifying Variance in Results for Fine-grained Visual Categorization.

Matthew Gwilliam, Adam Teuscher, Connor Anderson, Ryan Farrell

Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2021

Rethinking Common Assumptions to Mitigate Racial Bias in Face Recognition Datasets.

Matthew Gwilliam, Srinidhi Hegde, Lade Tinubu, Alex Hanson

Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2021

Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation.

Eva Vanmassenhove, Dimitar Sht. Shterionov, Matthew Gwilliam

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, 2021

2020

Facing the Hard Problems in FGVC.

Connor Anderson, Matthew Gwilliam, Adam Teuscher, Andrew Merrill, Ryan Farrell

CoRR, 2020

Intelligent Image Collection: Building the Optimal Dataset.

Matthew Gwilliam, Ryan Farrell

Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2020