Search Results for author:
Found 8 papers, 2 papers with code
Improving Fractal Pre-training
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
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.
Facing the Hard Problems in FGVC
In fine-grained visual categorization (FGVC), there is a near-singular focus in pursuit of attaining state-of-the-art (SOTA) accuracy.
Semantic Network Interpretation
For filter-level interpretation, we represent the concepts a filter encodes with a probability distribution of visual attributes.
Aligned to the Object, not to the Image: A Unified Pose-aligned Representation for Fine-grained Recognition
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 #17 on
Fine-Grained Image Classification
on NABirds
(using extra training data)
Pairwise Confusion for Fine-Grained Visual Classification
Fine-Grained Visual Classification (FGVC) datasets contain small sample sizes, along with significant intra-class variation and inter-class similarity.
Ranked #16 on
Fine-Grained Image Classification
on Stanford Dogs
Building a Bird Recognition App and Large Scale Dataset With Citizen Scientists: The Fine Print in Fine-Grained Dataset Collection
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%.
Deformable Part Descriptors for Fine-grained Recognition and Attribute Prediction
Recognizing objects in fine-grained domains can be extremely challenging due to the subtle differences between subcategories.
Ranked #25 on
Fine-Grained Image Classification
on CUB-200-2011
