Search Results for author: Ryan Farrell

Found 8 papers, 2 papers with code

Improving Fractal Pre-training

1 code implementation6 Oct 2021 Connor Anderson, Ryan Farrell

Challenging issues with large-scale image datasets become points of elegance for fractal pre-training: perfect label accuracy at zero cost; no need to store/transmit large image archives; no privacy/demographic bias/concerns of inappropriate content, as no humans are pictured; limitless supply and diversity of images; and the images are free/open-source.

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

no code implementations7 Sep 2021 Matthew Gwilliam, Adam Teuscher, Connor Anderson, Ryan Farrell

From this analysis, we both highlight the importance of reporting and comparing methods based on information beyond overall accuracy, as well as point out techniques that mitigate variance in FGVC results.

Fine-Grained Visual Categorization Image Classification

Facing the Hard Problems in FGVC

no code implementations23 Jun 2020 Connor Anderson, Matt Gwilliam, Adam Teuscher, Andrew Merrill, Ryan Farrell

In fine-grained visual categorization (FGVC), there is a near-singular focus in pursuit of attaining state-of-the-art (SOTA) accuracy.

Fine-Grained Visual Categorization

Semantic Network Interpretation

no code implementations23 May 2018 Pei Guo, Ryan Farrell

For filter-level interpretation, we represent the concepts a filter encodes with a probability distribution of visual attributes.

Network Interpretation Sentence

Aligned to the Object, not to the Image: A Unified Pose-aligned Representation for Fine-grained Recognition

no code implementations27 Jan 2018 Pei Guo, Ryan Farrell

Rather than representing an object by regions aligned to image axes, the proposed representation characterizes appearance relative to the object's pose using pose-aligned patches whose features are robust to variations in pose, scale and rotation.

Ranked #18 on Fine-Grained Image Classification on NABirds (using extra training data)

Fine-Grained Image Classification Object +1

Building a Bird Recognition App and Large Scale Dataset With Citizen Scientists: The Fine Print in Fine-Grained Dataset Collection

no code implementations CVPR 2015 Grant Van Horn, Steve Branson, Ryan Farrell, Scott Haber, Jessie Barry, Panos Ipeirotis, Pietro Perona, Serge Belongie

We worked with bird experts to measure the quality of popular datasets like CUB-200-2011 and ImageNet and found class label error rates of at least 4%.

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