Publications (7)
Improving Fractal Pre-training
Connor Anderson, Ryan Farrell
The deep neural networks used in modern computer vision systems require enormous image datasets to train them. These carefully-curated datasets typically have a million or more ima…
Semantic Network Interpretation
Pei Guo, Ryan Farrell
Network interpretation as an effort to reveal the features learned by a network remains largely visualization-based. In this paper, our goal is to tackle semantic network interpret…
Pairwise Confusion for Fine-Grained Visual Classification
Abhimanyu Dubey, Otkrist Gupta, Pei Guo +3
Fine-Grained Visual Classification (FGVC) datasets contain small sample sizes, along with significant intra-class variation and inter-class similarity. While prior work has address…
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…
LMPNet for Weakly-supervised Keypoint Discovery
Pei Guo, Ryan Farrell
In this work, we explore the task of semantic object keypoint discovery weakly-supervised by only category labels. This is achieved by transforming discriminatively-trained interme…