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

Showing 251300 of 353 papers

TitleStatusHype
Graph-propagation based Correlation Learning for Weakly Supervised Fine-grained Image Classification0
The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image ClassificationCode1
Weakly Supervised Attention Pyramid Convolutional Neural Network for Fine-Grained Visual Classification0
Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural NetworkCode1
Are These Birds Similar: Learning Branched Networks for Fine-grained RepresentationsCode1
Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual FeaturesCode1
Multimodal Semantic Transfer from Text to Image. Fine-Grained Image Classification by Distributional Semantics0
DAF-NET: a saliency based weakly supervised method of dual attention fusion for fine-grained image classification0
Competing Ratio Loss for Discriminative Multi-class Image ClassificationCode0
Big Transfer (BiT): General Visual Representation LearningCode2
Fine-grained Recognition: Accounting for Subtle Differences between Similar Classes0
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 Networks0
Fine-Grained Visual Classification with Batch Confusion Norm0
ACE: Adaptive Confusion Energy for Natural World Data Distribution0
Learning a Mixture of Granularity-Specific Experts for Fine-Grained CategorizationCode0
Selective Sparse Sampling for Fine-Grained Image RecognitionCode0
Siamese Networks: The Tale of Two Manifolds0
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 Classification0
Low-Rank Pairwise Alignment Bilinear Network For Few-Shot Fine-Grained Image Classification0
Competing Ratio Loss for Discriminative Multi-class Image Classification0
Few-shot Learning for Domain-specific Fine-grained Image Classification0
Understanding More about Human and Machine Attention in Deep Neural Networks0
Fixing the train-test resolution discrepancyCode2
Presence-Only Geographical Priors for Fine-Grained Image ClassificationCode1
Pay Attention to Convolution Filters: Towards Fast and Accurate Fine-Grained Transfer Learning0
Geo-Aware Networks for Fine-Grained RecognitionCode0
IP102: A Large-Scale Benchmark Dataset for Insect Pest RecognitionCode0
Destruction and Construction Learning for Fine-Grained Image RecognitionCode1
EfficientNet: Rethinking Model Scaling for Convolutional Neural NetworksCode3
Contextual Recurrent Convolutional Model for Robust Visual Learning0
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 Detection0
Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification from the Bottom UpCode0
Unsupervised Part Mining for Fine-grained Image Classification0
See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual ClassificationCode1
Fine-Grained Vehicle Classification with Unsupervised Parts Co-occurrence Learning0
Attribute-Aware Attention Model for Fine-grained Representation LearningCode0
Learning from Web Data: the Benefit of Unsupervised Object Localization0
Maximum-Entropy Fine Grained Classification0
Domain Adaptive Transfer Learning with Specialist Models0
GPipe: Efficient Training of Giant Neural Networks using Pipeline ParallelismCode2
Maximum-Entropy Fine-Grained Classification0
Learning to Navigate for Fine-grained ClassificationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TResnet-L + PMDAccuracy97.3Unverified
2CMAL-NetAccuracy97.1Unverified
3I2-HOFIAccuracy96.92Unverified
4TResNet-L + ML-DecoderAccuracy96.41Unverified
5DATAccuracy96.2Unverified
6ALIGNAccuracy96.13Unverified
7SR-GNNAccuracy96.1Unverified
8EffNet-L2 (SAM)Accuracy95.96Unverified
9SaSPA + CALAccuracy95.72Unverified
10CAPAccuracy95.7Unverified
#ModelMetricClaimedVerifiedStatus
1I2-HOFIAccuracy96.42Unverified
2SR-GNNAccuracy95.4Unverified
3Inceptionv4Accuracy95.11Unverified
4CAPAccuracy94.9Unverified
5CMAL-NetAccuracy94.7Unverified
6TBMSL-NetAccuracy94.7Unverified
7CSQA-NetAccuracy94.7Unverified
8PARTAccuracy94.6Unverified
9AENetAccuracy94.5Unverified
10SaSPA + CALAccuracy94.5Unverified
#ModelMetricClaimedVerifiedStatus
1HERBSAccuracy93.1Unverified
2PIMAccuracy92.8Unverified
3MDCMAccuracy92.5Unverified
4SFETransAccuracy91.8Unverified
5CAPAccuracy91.8Unverified
6IELTAccuracy91.8Unverified
7TransFGAccuracy91.7Unverified
8SWAG (ViT H/14)Accuracy91.7Unverified
9ViT-NeTAccuracy91.7Unverified
10FFVTAccuracy91.6Unverified
#ModelMetricClaimedVerifiedStatus
1HERBSAccuracy93Unverified
2MetaFormer (MetaFormer-2,384)Accuracy93Unverified
3PIMAccuracy92.8Unverified
4ViT-NeT (SwinV2-B)Accuracy92.5Unverified
5MPSAAccuracy92.5Unverified
6CSQA-NetAccuracy92.3Unverified
7I2-HOFIAccuracy92.12Unverified
8MDCMAccuracy92Unverified
9CGLAccuracy91.7Unverified
10SR-GNNAccuracy91.2Unverified