Matthew Gwilliam

According to our database, Matthew Gwilliam authored at least 28 papers between 2020 and 2026.

Collaborative distances:

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

2026

Going Down Memory Lane: Scaling Tokens for Video Stream Understanding with Dynamic KV-Cache Memory.

Vatsal Agarwal, Saksham Suri, Matthew Gwilliam, Pulkit Kumar, Abhinav Shrivastava

CoRR, February, 2026

TeCoNeRV: Leveraging Temporal Coherence for Compressible Neural Representations for Videos.

Namitha Padmanabhan, Matthew Gwilliam, Abhinav Shrivastava

CoRR, February, 2026

Towards Understanding Best Practices for Quantization of Vision-Language Models.

Gautom Das, Vincent La, Ethan Lau, Abhinav Shrivastava, Matthew Gwilliam

CoRR, January, 2026

Implicit Neural Representation Facilitates Unified Universal Vision Encoding.

Matthew Gwilliam, Xiao Wang, Xuefeng Hu, Zhenheng Yang

CoRR, January, 2026

How to Design and Train Your Implicit Neural Representation for Video Compression.

Matthew Gwilliam, Roy Zhang, Namitha Padmanabhan, Hongyang Du, Abhinav Shrivastava

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2026

2025

AgentComp: From Agentic Reasoning to Compositional Mastery in Text-to-Image Models.

Arman Zarei, Jiacheng Pan, Matthew Gwilliam, Soheil Feizi, Zhenheng Yang

CoRR, December, 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, July, 2025

Evolutionary Caching to Accelerate Your Off-the-Shelf Diffusion Model.

Anirud Aggarwal, Abhinav Shrivastava, Matthew Gwilliam

CoRR, June, 2025

Utilization of Neighbor Information for Image Classification with Different Levels of Supervision.

Gihan Jayatilaka, Abhinav Shrivastava, Matthew Gwilliam

CoRR, March, 2025

Accelerate High-Quality Diffusion Models with Inner Loop Feedback.

Matthew Gwilliam, Han Cai, Di Wu, Abhinav Shrivastava, Zhiyu Cheng

CoRR, January, 2025

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

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2025

2024

NeRF-Aug: Data Augmentation for Robotics with Neural Radiance Fields.

Eric Zhu, Mara Levy, Matthew Gwilliam, 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

Quantifying NBA Shot Quality: A Deep Network Approach.

Archit Ramanasai Kambhamettu, Abhinav Shrivastava, Matthew Gwilliam

Proceedings of the 7th ACM International Workshop on Multimedia Content Analysis in Sports, 2024

Do Text-Free Diffusion Models Learn Discriminative Visual Representations?

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

Proceedings of the Computer Vision - ECCV 2024, 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

Proceedings of the Computer Vision - ECCV 2024, 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

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

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