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
Most implemented papers
Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition
xcnkx/fine_grained_classification
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ECCV 2018
Attention-based learning for fine-grained image recognition remains a challenging task, where most of the existing methods treat each object part in isolation, while neglecting the correlations among them.
Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic Embedding
HCPLab-SYSU/HSE
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In this work, we investigate simultaneously predicting categories of different levels in the hierarchy and integrating this structured correlation information into the deep neural network by developing a novel Hierarchical Semantic Embedding (HSE) framework.
Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image Recognition
Learning subtle yet discriminative features (e. g., beak and eyes for a bird) plays a significant role in fine-grained image recognition.
Deep Learning for Fine-Grained Image Analysis: A Survey
Sorananakiii/Fine-grained-image-recognition
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Among various research areas of CV, fine-grained image analysis (FGIA) is a longstanding and fundamental problem, and has become ubiquitous in diverse real-world applications.
Pay attention to the activations: a modular attention mechanism for fine-grained image recognition
Attention has been typically implemented in neural networks by selecting the most informative regions of the image that improve classification.
Selective Sparse Sampling for Fine-Grained Image Recognition
Yao-DD/S3N
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ICCV 2019
Fine-grained recognition poses the unique challenge of capturing subtle inter-class differences under considerable intra-class variances (e. g., beaks for bird species).
Learning Deep Bilinear Transformation for Fine-grained Image Representation
researchmm/DBTNet
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NeurIPS 2019
However, the computational cost to learn pairwise interactions between deep feature channels is prohibitively expensive, which restricts this powerful transformation to be used in deep neural networks.
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.
Semi-Supervised Recognition under a Noisy and Fine-grained Dataset
One of the difficulties of this competition is how to use unlabeled data.
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.
