SOTAVerified

Fine-Grained Image Classification

Fine-Grained Image Classification is a task in computer vision where the goal is to classify images into subcategories within a larger category. For example, classifying different species of birds or different types of flowers. This task is considered to be fine-grained because it requires the model to distinguish between subtle differences in visual appearance and patterns, making it more challenging than regular image classification tasks.

( Image credit: Looking for the Devil in the Details )

Papers

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Showing 251–300 of 353 papers

TitleStatusHype
A new dataset of dog breed images and a benchmark for fine-grained classification—0
Contrastively-reinforced Attention Convolutional Neural Network for Fine-grained Image RecognitionCode0
Data-driven Meta-set Based Fine-Grained Visual ClassificationCode0
ReDro: Efficiently Learning Large-sized SPD Visual Representation—0
Rethinking Generative Zero-Shot Learning: An Ensemble Learning Perspective for Recognising Visual Patches—0
Hallucinating Saliency Maps for Fine-Grained Image Classification for Limited Data Domains—0
Maximum Entropy Regularization and Chinese Text Recognition—0
End-to-end Learning of a Fisher Vector Encoding for Part Features in Fine-grained RecognitionCode0
Performing Image Classification for 10 Different Monkey Species using CNN—0
RP2K: A Large-Scale Retail Product Dataset for Fine-Grained Image Classification—0
Fine-Tuning DARTS for Image Classification—0
Weakly Supervised Fine-Grained Image Classification via Guassian Mixture Model Oriented Discriminative Learning—0
Fine-Grained Visual Classification with Efficient End-to-end Localization—0
Attribute Mix: Semantic Data Augmentation for Fine Grained Recognition—0
Group Based Deep Shared Feature Learning for Fine-grained Image Classification—0
Graph-propagation based Correlation Learning for Weakly Supervised Fine-grained Image Classification—0
Weakly Supervised Attention Pyramid Convolutional Neural Network for Fine-Grained Visual Classification—0
Multimodal Semantic Transfer from Text to Image. Fine-Grained Image Classification by Distributional Semantics—0
DAF-NET: a saliency based weakly supervised method of dual attention fusion for fine-grained image classification—0
Competing Ratio Loss for Discriminative Multi-class Image ClassificationCode0
Fine-grained Recognition: Accounting for Subtle Differences between Similar Classes—0
Rethinking Softmax with Cross-Entropy: Neural Network Classifier as Mutual Information EstimatorCode0
ELoPE: Fine-Grained Visual Classification with Efficient Localization, Pooling and EmbeddingCode0
Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual RecognitionCode0
A Spectral Nonlocal Block for Neural Networks—0
ACE: Adaptive Confusion Energy for Natural World Data Distribution—0
Fine-Grained Visual Classification with Batch Confusion Norm—0
Learning a Mixture of Granularity-Specific Experts for Fine-Grained CategorizationCode0
Siamese Networks: The Tale of Two Manifolds—0
Selective Sparse Sampling for Fine-Grained Image RecognitionCode0
Attention Convolutional Binary Neural Tree for Fine-Grained Visual CategorizationCode0
Classification-Specific Parts for Improving Fine-Grained Visual CategorizationCode0
Part-Guided Attention Learning for Vehicle Instance RetrievalCode0
Cross-X Learning for Fine-Grained Visual CategorizationCode0
Coarse2Fine: A Two-stage Training Method for Fine-grained Visual Classification—0
Low-Rank Pairwise Alignment Bilinear Network For Few-Shot Fine-Grained Image Classification—0
Competing Ratio Loss for Discriminative Multi-class Image Classification—0
Few-shot Learning for Domain-specific Fine-grained Image Classification—0
Understanding More about Human and Machine Attention in Deep Neural Networks—0
Pay Attention to Convolution Filters: Towards Fast and Accurate Fine-Grained Transfer Learning—0
Geo-Aware Networks for Fine-Grained RecognitionCode0
IP102: A Large-Scale Benchmark Dataset for Insect Pest RecognitionCode0
Contextual Recurrent Convolutional Model for Robust Visual Learning—0
Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image RecognitionCode0
Spatial-Aware Non-Local Attention for Fashion Landmark Detection—0
Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification from the Bottom UpCode0
Unsupervised Part Mining for Fine-grained Image Classification—0
Fine-Grained Vehicle Classification with Unsupervised Parts Co-occurrence Learning—0
Attribute-Aware Attention Model for Fine-grained Representation LearningCode0
Learning from Web Data: the Benefit of Unsupervised Object Localization—0
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All datasetsStanford CarsFGVC-AircraftCUB-200-2011NABirdsOxford 102 FlowersStanford DogsOxford-IIIT PetsCaltech-101Food-101Oxford-IIIT Pet DatasetCompCarsBird-225

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TResnet-L + PMDAccuracy97.3—Unverified
2CMAL-NetAccuracy97.1—Unverified
3I2-HOFIAccuracy96.92—Unverified
4TResNet-L + ML-DecoderAccuracy96.41—Unverified
5DATAccuracy96.2—Unverified
6ALIGNAccuracy96.13—Unverified
7SR-GNNAccuracy96.1—Unverified
8EffNet-L2 (SAM)Accuracy95.96—Unverified
9SaSPA + CALAccuracy95.72—Unverified
10CAPAccuracy95.7—Unverified
#ModelMetricClaimedVerifiedStatus
1I2-HOFIAccuracy96.42—Unverified
2SR-GNNAccuracy95.4—Unverified
3Inceptionv4Accuracy95.11—Unverified
4CAPAccuracy94.9—Unverified
5CMAL-NetAccuracy94.7—Unverified
6TBMSL-NetAccuracy94.7—Unverified
7CSQA-NetAccuracy94.7—Unverified
8PARTAccuracy94.6—Unverified
9AENetAccuracy94.5—Unverified
10SaSPA + CALAccuracy94.5—Unverified
#ModelMetricClaimedVerifiedStatus
1HERBSAccuracy93.1—Unverified
2PIMAccuracy92.8—Unverified
3MDCMAccuracy92.5—Unverified
4SFETransAccuracy91.8—Unverified
5CAPAccuracy91.8—Unverified
6IELTAccuracy91.8—Unverified
7TransFGAccuracy91.7—Unverified
8SWAG (ViT H/14)Accuracy91.7—Unverified
9ViT-NeTAccuracy91.7—Unverified
10FFVTAccuracy91.6—Unverified
#ModelMetricClaimedVerifiedStatus
1HERBSAccuracy93—Unverified
2MetaFormer (MetaFormer-2,384)Accuracy93—Unverified
3PIMAccuracy92.8—Unverified
4ViT-NeT (SwinV2-B)Accuracy92.5—Unverified
5MPSAAccuracy92.5—Unverified
6CSQA-NetAccuracy92.3—Unverified
7I2-HOFIAccuracy92.12—Unverified
8MDCMAccuracy92—Unverified
9CGLAccuracy91.7—Unverified
10SR-GNNAccuracy91.2—Unverified