Fine-Grained Visual Categorization
26 papers with code • 0 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in Fine-Grained Visual Categorization
MTL-AQA
VegFru
FoodX-251
L-Bird
FeathersV1
Most implemented papers
Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning
richardaecn/cvpr18-inaturalist-transfer
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CVPR 2018
We propose a measure to estimate domain similarity via Earth Mover's Distance and demonstrate that transfer learning benefits from pre-training on a source domain that is similar to the target domain by this measure.
FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery
kkanshul/finegan
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CVPR 2019
We propose FineGAN, a novel unsupervised GAN framework, which disentangles the background, object shape, and object appearance to hierarchically generate images of fine-grained object categories.
FoodX-251: A Dataset for Fine-grained Food Classification
Food classification is a challenging problem due to the large number of categories, high visual similarity between different foods, as well as the lack of datasets for training state-of-the-art deep models.
ePillID Dataset: A Low-Shot Fine-Grained Benchmark for Pill Identification
Identifying prescription medications is a frequent task for patients and medical professionals; however, this is an error-prone task as many pills have similar appearances (e. g. white round pills), which increases the risk of medication errors.
Benchmark Platform for Ultra-Fine-Grained Visual Categorization Beyond Human Performance
The proposed UFG image dataset and evaluation protocols is intended to serve as a benchmark platform that can advance research of visual classification from approaching human performance to beyond human ability, via facilitating benchmark data of artificial intelligence (AI) not to be limited by the labels of human intelligence (HI).
Benchmarking Representation Learning for Natural World Image Collections
In order to facilitate progress in this area we present two new natural world visual classification datasets, iNat2021 and NeWT.
Self-Supervised Learning for Fine-Grained Visual Categorization
The deconstruction learning forces the model to focus on local object parts, while reconstruction learning helps in learning the correlation between the parts.
Feature Fusion Vision Transformer for Fine-Grained Visual Categorization
Markin-Wang/FFVT
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We verify the effectiveness of FFVT on three benchmarks where FFVT achieves the state-of-the-art performance.
Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification
Unlike most existing methods that learn visual attention based on conventional likelihood, we propose to learn the attention with counterfactual causality, which provides a tool to measure the attention quality and a powerful supervisory signal to guide the learning process.
High-Order-Interaction for weakly supervised Fine-Grained Visual Categorization
puallee/HOI-Net
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Neurocomputing 2021
Of those, methods based on bilinear pooling are one of the main categories for computing the interaction between deep features and have shown high effectiveness.
