Matthew Gwilliam

Orcid: 0000-0001-9826-6285

According to our database1, Matthew Gwilliam authored at least 19 papers between 2020 and 2025.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2025
Accelerate High-Quality Diffusion Models with Inner Loop Feedback.
CoRR, January, 2025

2024
NeRF-Aug: Data Augmentation for Robotics with Neural Radiance Fields.
CoRR, 2024

Elusive Images: Beyond Coarse Analysis for Fine-Grained Recognition.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

Quantifying NBA Shot Quality: A Deep Network Approach.
Proceedings of the 7th ACM International Workshop on Multimedia Content Analysis in Sports, 2024

Do Text-Free Diffusion Models Learn Discriminative Visual Representations?
Proceedings of the Computer Vision - ECCV 2024, 2024

Latent-INR: A Flexible Framework for Implicit Representations of Videos with Discriminative Semantics.
Proceedings of the Computer Vision - ECCV 2024, 2024

Explaining the Implicit Neural Canvas: Connecting Pixels to Neurons by Tracing Their Contributions.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023
A Video is Worth 10, 000 Words: Training and Benchmarking with Diverse Captions for Better Long Video Retrieval.
CoRR, 2023

Do text-free diffusion models learn discriminative visual representations?
CoRR, 2023

Diffusion Models Beat GANs on Image Classification.
CoRR, 2023

HNeRV: A Hybrid Neural Representation for Videos.
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.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

CNeRV: Content-adaptive Neural Representation for Visual Data.
Proceedings of the 33rd British Machine Vision Conference 2022, 2022

2021
Fair Comparison: Quantifying Variance in Resultsfor Fine-grained Visual Categorization.
CoRR, 2021

Fair Comparison: Quantifying Variance in Results for Fine-grained Visual Categorization.
Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2021

Rethinking Common Assumptions to Mitigate Racial Bias in Face Recognition Datasets.
Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2021

Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation.
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.
CoRR, 2020

Intelligent Image Collection: Building the Optimal Dataset.
Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2020


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