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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–150 of 353 papers

TitleStatusHype
Concept Learners for Few-Shot LearningCode1
Escaping the Big Data Paradigm with Compact TransformersCode1
Making a Bird AI Expert Work for You and MeCode1
Learning to Navigate for Fine-grained ClassificationCode1
Your "Flamingo" is My "Bird": Fine-Grained, or NotCode1
Proxy Anchor Loss for Deep Metric LearningCode1
Exploring Vision Transformers for Fine-grained ClassificationCode1
Label Relation Graphs Enhanced Hierarchical Residual Network for Hierarchical Multi-Granularity ClassificationCode1
Fine-Grained Predicates Learning for Scene Graph GenerationCode1
Knowledge Mining with Scene Text for Fine-Grained RecognitionCode1
Generative Parameter-Efficient Fine-TuningCode1
Learning Attentive Pairwise Interaction for Fine-Grained ClassificationCode1
Feature Fusion Vision Transformer for Fine-Grained Visual CategorizationCode1
Learning with Unmasked Tokens Drives Stronger Vision LearnersCode1
ML-Decoder: Scalable and Versatile Classification HeadCode1
Learn from Each Other to Classify Better: Cross-layer Mutual Attention Learning for Fine-grained Visual ClassificationCode1
Convolutional Fine-Grained Classification with Self-Supervised Target Relation RegularizationCode1
PDiscoFormer: Relaxing Part Discovery Constraints with Vision TransformersCode1
Transformer in TransformerCode1
Danish Fungi 2020 -- Not Just Another Image Recognition DatasetCode1
Fine-Grained Image Classification via Combining Vision and Language—0
Automatic Fine-grained Glomerular Lesion Recognition in Kidney Pathology—0
Fine-grained Image Classification by Exploring Bipartite-Graph Labels—0
Cross-Hierarchical Bidirectional Consistency Learning for Fine-Grained Visual Classification—0
Fine-Grained Few Shot Learning with Foreground Object Transformation—0
Cross-layer Navigation Convolutional Neural Network for Fine-grained Visual Classification—0
Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN—0
Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks—0
Fine-grained Classification via Categorical Memory Networks—0
Adaptive Classification of Interval-Valued Time Series—0
Learning Deep Classifiers Consistent With Fine-Grained Novelty Detection—0
Fine-grained Classification of Solder Joints with α-skew Jensen-Shannon Divergence—0
Fine-graind Image Classification via Combining Vision and Language—0
Few-shot Learning for Domain-specific Fine-grained Image Classification—0
Convolutional Low-Resolution Fine-Grained Classification—0
Adaptive Fine-Grained Predicates Learning for Scene Graph Generation—0
Feature Channel Adaptive Enhancement for Fine-Grained Visual Classification—0
A Unified Framework to Analyze and Design the Nonlocal Blocks for Neural Networks—0
Leaf Cultivar Identification via Prototype-enhanced Learning—0
Learning from Web Data: the Benefit of Unsupervised Object Localization—0
Fast Fine-grained Image Classification via Weakly Supervised Discriminative Localization—0
Contextual Recurrent Convolutional Model for Robust Visual Learning—0
Knowledge-Embedded Representation Learning for Fine-Grained Image Recognition—0
Exploring Target Driven Image Classification—0
Context-Semantic Quality Awareness Network for Fine-Grained Visual Categorization—0
Integrating Scene Text and Visual Appearance for Fine-Grained Image Classification—0
Alignment Enhancement Network for Fine-grained Visual Categorization—0
Interpretable Attention Guided Network for Fine-grained Visual Classification—0
Large Neural Networks Learning from Scratch with Very Few Data and without Explicit Regularization—0
Assessing The Importance Of Colours For CNNs In Object Recognition—0
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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