Fine-Grained Image Recognition
33 papers with code • 4 benchmarks • 9 datasets
Benchmarks
These leaderboards are used to track progress in Fine-Grained Image Recognition
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Datasets
CUB-200-2011
OmniBenchmark
OVEN
WebFG-496
CUB-GHA
Goldfinch
WikiChurches
CNFOOD-241-Chen
Latest papers
Fine-grained Hand Gesture Recognition in Multi-viewpoint Hand Hygiene
willogy-team/hand-gesture-recognition-smc2021
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This paper contributes a new high-quality dataset for hand gesture recognition in hand hygiene systems, named "MFH".
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.
Exploring Localization for Self-supervised Fine-grained Contrastive Learning
Self-supervised contrastive learning has demonstrated great potential in learning visual representations.
Local Patch AutoAugment with Multi-Agent Collaboration
kornia/kornia
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We formulate it as a multi-agent reinforcement learning (MARL) problem, where each agent learns an augmentation policy for each patch based on its content together with the semantics of the whole image.
Danish Fungi 2020 -- Not Just Another Image Recognition Dataset
Interestingly, ViT achieves results superior to CNN baselines with 80. 45% accuracy and 0. 743 macro F1 score, reducing the CNN error by 9% and 12% respectively.
Neural Prototype Trees for Interpretable Fine-grained Image Recognition
M-Nauta/ProtoTree
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CVPR 2021
We propose the Neural Prototype Tree (ProtoTree), an intrinsically interpretable deep learning method for fine-grained image recognition.
How Well Do Self-Supervised Models Transfer?
linusericsson/ssl-transfer
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CVPR 2021
We evaluate the transfer performance of 13 top self-supervised models on 40 downstream tasks, including many-shot and few-shot recognition, object detection, and dense prediction.
Contrastively-reinforced Attention Convolutional Neural Network for Fine-grained Image Recognition
The evaluation information is backpropagated and forces the classification stream to improve its awareness of visual attention, which helps classification.
Semi-Supervised Recognition under a Noisy and Fine-grained Dataset
One of the difficulties of this competition is how to use unlabeled data.
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.
