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 151–200 of 353 papers

TitleStatusHype
DCNN: Dual Cross-current Neural Networks Realized Using An Interactive Deep Learning Discriminator for Fine-grained Objects—0
Deep Neural Network Models Trained With A Fixed Random Classifier Transfer Better Across Domains—0
Deep Neural Networks Fused with Textures for Image Classification—0
Deep Quantization: Encoding Convolutional Activations with Deep Generative Model—0
Deformable Part Descriptors for Fine-grained Recognition and Attribute Prediction—0
Delving into Multimodal Prompting for Fine-grained Visual Classification—0
Detecting Visually Relevant Sentences for Fine-Grained Classification—0
Dining on Details: LLM-Guided Expert Networks for Fine-Grained Food Recognition—0
Do Better ImageNet Models Transfer Better?—0
Domain Adaptive Transfer Learning on Visual Attention Aware Data Augmentation for Fine-grained Visual Categorization—0
Domain Adaptive Transfer Learning with Specialist Models—0
Dual Cross-Attention Learning for Fine-Grained Visual Categorization and Object Re-Identification—0
Embedding Label Structures for Fine-Grained Feature Representation—0
Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors—0
Enhancing Fine-grained Image Classification through Attentive Batch Training—0
Enhancing Fine-Grained Image Classifications via Cascaded Vision Language Models—0
Enhancing Multimodal In-Context Learning for Image Classification through Coreset Optimization—0
Exploring Target Driven Image Classification—0
Fast Fine-grained Image Classification via Weakly Supervised Discriminative Localization—0
Feature Channel Adaptive Enhancement for Fine-Grained Visual Classification—0
Few-shot Learning for Domain-specific Fine-grained Image Classification—0
Fine-graind Image Classification via Combining Vision and Language—0
Fine-grained Classification of Solder Joints with α-skew Jensen-Shannon Divergence—0
Fine-grained Classification via Categorical Memory Networks—0
Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks—0
Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN—0
Fine-Grained Few Shot Learning with Foreground Object Transformation—0
Fine-grained Image Classification by Exploring Bipartite-Graph Labels—0
Fine-Grained Image Classification via Combining Vision and Language—0
Fine-grained Recognition: Accounting for Subtle Differences between Similar Classes—0
Fine-Grained Recognition as HSnet Search for Informative Image Parts—0
Fine-grained Recognition Datasets for Biodiversity Analysis—0
0/1 Deep Neural Networks via Block Coordinate Descent—0
Fine-Grained Vehicle Classification with Unsupervised Parts Co-occurrence Learning—0
Fine-Grained Visual Classification of Aircraft—0
Fine-Grained Visual Classification with Efficient End-to-end Localization—0
Fine-Grained Visual Classification with Batch Confusion Norm—0
ACE: Adaptive Confusion Energy for Natural World Data Distribution—0
Fine-Tuning DARTS for Image Classification—0
Gaze Embeddings for Zero-Shot Image Classification—0
Generalized BackPropagation, Étude De Cas: Orthogonality—0
Generating Counterfactual Explanations with Natural Language—0
GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers—0
Grad-CAM guided channel-spatial attention module for fine-grained visual classification—0
Grafit: Learning fine-grained image representations with coarse labels—0
Graph-Based High-Order Relation Discovery for Fine-Grained Recognition—0
Graph-propagation based Correlation Learning for Weakly Supervised Fine-grained Image Classification—0
Grassmann Pooling as Compact Homogeneous Bilinear Pooling for Fine-Grained Visual Classification—0
Group Based Deep Shared Feature Learning for Fine-grained Image Classification—0
Hallucinating Saliency Maps for Fine-Grained Image Classification for Limited Data Domains—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
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