13 citations · 33 across the 5 of their papers we have counts for
4 papers · 1 filter
CNeRV: Content-adaptive Neural Representation for Visual Data
Hao Chen, Matt Gwilliam, Bo He +2
Compression and reconstruction of visual data have been widely studied in the computer vision community, even before the popularization of deep learning. More recently, some have u…
Rethinking Common Assumptions to Mitigate Racial Bias in Face Recognition Datasets
Matthew Gwilliam, Srinidhi Hegde, Lade Tinubu +1
Many existing works have made great strides towards reducing racial bias in face recognition. However, most of these methods attempt to rectify bias that manifests in models during…
Fair Comparison: Quantifying Variance in Resultsfor Fine-grained Visual Categorization
Matthew Gwilliam, Adam Teuscher, Connor Anderson +1
For the task of image classification, researchers work arduously to develop the next state-of-the-art (SOTA) model, each bench-marking their own performance against that of their p…
Facing the Hard Problems in FGVC
Connor Anderson, Matt Gwilliam, Adam Teuscher +2
In fine-grained visual categorization (FGVC), there is a near-singular focus in pursuit of attaining state-of-the-art (SOTA) accuracy. This work carefully analyzes the performance…