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 201250 of 353 papers

TitleStatusHype
Fine-Grained Visual Classification via Simultaneously Learning of Multi-regional Multi-grained FeaturesCode1
Grad-CAM guided channel-spatial attention module for fine-grained visual classification0
Progressive Co-Attention Network for Fine-grained Visual ClassificationCode1
Context-aware Attentional Pooling (CAP) for Fine-grained Visual ClassificationCode1
Exploring Target Driven Image Classification0
Auto-view contrastive learning for few-shot image recognition0
Natural World Distribution via Adaptive Confusion Energy Regularization0
A Unified Framework to Analyze and Design the Nonlocal Blocks for Neural Networks0
Training data-efficient image transformers & distillation through attentionCode1
Knowledge Transfer Based Fine-grained Visual ClassificationCode0
Fine-grained Classification via Categorical Memory Networks0
Assessing The Importance Of Colours For CNNs In Object Recognition0
SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained DataCode1
BSNet: Bi-Similarity Network for Few-shot Fine-grained Image ClassificationCode1
Grafit: Learning fine-grained image representations with coarse labels0
Learning Class Unique Features in Fine-Grained Visual Classification0
Your "Flamingo" is My "Bird": Fine-Grained, or NotCode1
Beyond the Attention: Distinguish the Discriminative and Confusable Features For Fine-grained Image Classification0
Domain Adaptive Transfer Learning on Visual Attention Aware Data Augmentation for Fine-grained Visual Categorization0
Sharpness-Aware Minimization for Efficiently Improving GeneralizationCode2
A new dataset of dog breed images and a benchmark for fine-grained classificationCode0
Multi-Modal Reasoning Graph for Scene-Text Based Fine-Grained Image Classification and RetrievalCode1
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 Representation0
Rethinking Generative Zero-Shot Learning: An Ensemble Learning Perspective for Recognising Visual Patches0
Hallucinating Saliency Maps for Fine-Grained Image Classification for Limited Data Domains0
Concept Learners for Few-Shot LearningCode1
Maximum Entropy Regularization and Chinese Text Recognition0
SpinalNet: Deep Neural Network with Gradual InputCode1
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 CNN0
ULSAM: Ultra-Lightweight Subspace Attention Module for Compact Convolutional Neural NetworksCode1
Learning Semantically Enhanced Feature for Fine-Grained Image ClassificationCode1
RP2K: A Large-Scale Retail Product Dataset for Fine-Grained Image Classification0
Fine-Tuning DARTS for Image Classification0
Weakly Supervised Fine-Grained Image Classification via Guassian Mixture Model Oriented Discriminative Learning0
Focus Longer to See Better:Recursively Refined Attention for Fine-Grained Image ClassificationCode1
Neural Architecture TransferCode1
Fine-Grained Visual Classification with Efficient End-to-end Localization0
Attribute Mix: Semantic Data Augmentation for Fine Grained RecognitionCode0
Group Based Deep Shared Feature Learning for Fine-grained Image Classification0
Gradient Centralization: A New Optimization Technique for Deep Neural NetworksCode1
Proxy Anchor Loss for Deep Metric LearningCode1
Look-into-Object: Self-supervised Structure Modeling for Object RecognitionCode1
TResNet: High Performance GPU-Dedicated ArchitectureCode1
Rethinking Depthwise Separable Convolutions: How Intra-Kernel Correlations Lead to Improved MobileNetsCode1
Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual CategorizationCode1
Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw PatchesCode1
Learning Attentive Pairwise Interaction 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