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
Stochastic Subsampling With Average Pooling—0
Streaming Self-Training via Domain-Agnostic Unlabeled Images—0
Structural feature enhanced transformer for fine-grained image recognition—0
Taxonomy-Aware Evaluation of Vision-Language Models—0
TDLS: A Top-Down Layer Searching Algorithm for Generating Counterfactual Visual Explanation—0
The 2021 Hotel-ID to Combat Human Trafficking Competition Dataset—0
The Application of Two-level Attention Models in Deep Convolutional Neural Network for Fine-grained Image Classification—0
Learning Class Unique Features in Fine-Grained Visual Classification—0
Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions—0
Transformer with Peak Suppression and Knowledge Guidance for Fine-grained Image Recognition—0
TransIFC: Invariant Cues-aware Feature Concentration Learning for Efficient Fine-grained Bird Image Classification—0
Unsupervised Learning using Pretrained CNN and Associative Memory Bank—0
Unsupervised Part Mining for Fine-grained Image Classification—0
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision—0
Semantically-Prompted Language Models Improve Visual Descriptions—0
ViT-FOD: A Vision Transformer based Fine-grained Object Discriminator—0
Weakly Supervised Attention Pyramid Convolutional Neural Network for Fine-Grained Visual Classification—0
Weakly Supervised Bilinear Attention Network for Fine-Grained Visual Classification—0
Weakly Supervised Fine-Grained Image Categorization—0
Weakly Supervised Fine-Grained Image Classification via Guassian Mixture Model Oriented Discriminative Learning—0
Webly Supervised Learning Meets Zero-Shot Learning: A Hybrid Approach for Fine-Grained Classification—0
When Does Contrastive Visual Representation Learning Work?—0
Where to Focus: Deep Attention-based Spatially Recurrent Bilinear Networks for Fine-Grained Visual Recognition—0
Zero-Shot Fine-Grained Classification by Deep Feature Learning with Semantics—0
Contrastively-reinforced Attention Convolutional Neural Network for Fine-grained Image RecognitionCode0
Competing Ratio Loss for Discriminative Multi-class Image ClassificationCode0
Rethinking Softmax with Cross-Entropy: Neural Network Classifier as Mutual Information EstimatorCode0
Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic EmbeddingCode0
Classification-Specific Parts for Improving Fine-Grained Visual CategorizationCode0
Enhancing Fine-Grained 3D Object Recognition using Hybrid Multi-Modal Vision Transformer-CNN ModelsCode0
Cascading Hierarchical Networks with Multi-task Balanced Loss for Fine-grained hashingCode0
Few-shot Fine-grained Image Classification via Multi-Frequency Neighborhood and Double-cross ModulationCode0
Few-Shot Classification of Interactive Activities of Daily Living (InteractADL)Code0
Extremely Fine-Grained Visual Classification over Resembling Glyphs in the WildCode0
Extract More from Less: Efficient Fine-Grained Visual Recognition in Low-Data RegimesCode0
Selective Sparse Sampling for Fine-Grained Image RecognitionCode0
Explored An Effective Methodology for Fine-Grained Snake RecognitionCode0
Evaluation of Output Embeddings for Fine-Grained Image ClassificationCode0
A Continual Development Methodology for Large-scale Multitask Dynamic ML SystemsCode0
A Large-Scale Car Dataset for Fine-Grained Categorization and VerificationCode0
Ensembles of Vision Transformers as a New Paradigm for Automated Classification in EcologyCode0
Bilinear CNNs for Fine-grained Visual RecognitionCode0
Bag of Tricks and a Strong Baseline for FGVCCode0
EnGraf-Net: Multiple Granularity Branch Network with Fine-Coarse Graft Grained for Classification TaskCode0
End-to-end Learning of a Fisher Vector Encoding for Part Features in Fine-grained RecognitionCode0
Understanding Gaussian Attention Bias of Vision Transformers Using Effective Receptive FieldsCode0
ELoPE: Fine-Grained Visual Classification with Efficient Localization, Pooling and EmbeddingCode0
DS_FusionNet: Dynamic Dual-Stream Fusion with Bidirectional Knowledge Distillation for Plant Disease RecognitionCode0
Universal Fine-grained Visual Categorization by Concept Guided LearningCode0
MergedNET: A simple approach for one-shot learning in siamese networks based on similarity layersCode0
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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
5CSQA-NetAccuracy94.7—Unverified
6CMAL-NetAccuracy94.7—Unverified
7TBMSL-NetAccuracy94.7—Unverified
8PARTAccuracy94.6—Unverified
9SaSPA + CALAccuracy94.5—Unverified
10AENetAccuracy94.5—Unverified
#ModelMetricClaimedVerifiedStatus
1HERBSAccuracy93.1—Unverified
2PIMAccuracy92.8—Unverified
3MDCMAccuracy92.5—Unverified
4IELTAccuracy91.8—Unverified
5CAPAccuracy91.8—Unverified
6SFETransAccuracy91.8—Unverified
7TransFGAccuracy91.7—Unverified
8ViT-NeTAccuracy91.7—Unverified
9SWAG (ViT H/14)Accuracy91.7—Unverified
10FFVTAccuracy91.6—Unverified
#ModelMetricClaimedVerifiedStatus
1MetaFormer (MetaFormer-2,384)Accuracy93—Unverified
2HERBSAccuracy93—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