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 301–350 of 353 papers

TitleStatusHype
Structural feature enhanced transformer for fine-grained image recognition—0
Coarse2Fine: A Two-stage Training Method for Fine-grained Visual Classification—0
Channel DropBlock: An Improved Regularization Method for Fine-Grained Visual Classification—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
CA-PMG: Channel attention and progressive multi-granularity training network for fine-grained visual classification—0
The Application of Two-level Attention Models in Deep Convolutional Neural Network for Fine-grained Image Classification—0
Bridge the Gap between Supervised and Unsupervised Learning for Fine-Grained Classification—0
Bilinear CNN Models for Fine-Grained Visual Recognition—0
Weakly Supervised Fine-Grained Image Categorization—0
Weakly Supervised Fine-Grained Image Classification via Guassian Mixture Model Oriented Discriminative Learning—0
Grad-CAM guided channel-spatial attention module for fine-grained visual classification—0
Grafit: Learning fine-grained image representations with coarse labels—0
Beyond the Attention: Distinguish the Discriminative and Confusable Features For Fine-grained Image Classification—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
Webly Supervised Learning Meets Zero-Shot Learning: A Hybrid Approach for Fine-Grained Classification—0
When Does Contrastive Visual Representation Learning Work?—0
How Quality Affects Deep Neural Networks in Fine-Grained Image Classification—0
Auto-view contrastive learning for few-shot image recognition—0
GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers—0
Learning Class Unique Features in Fine-Grained Visual Classification—0
Understanding More about Human and Machine Attention in Deep Neural Networks—0
Hybrid Feature Collaborative Reconstruction Network for Few-Shot Fine-Grained Image Classification—0
Hyper-Class Augmented and Regularized Deep Learning for Fine-Grained Image Classification—0
Generating Counterfactual Explanations with Natural Language—0
Generalized BackPropagation, Étude De Cas: Orthogonality—0
Improved Robustness of Vision Transformer via PreLayerNorm in Patch Embedding—0
Gaze Embeddings for Zero-Shot Image Classification—0
10,000+ Times Accelerated Robust Subset Selection (ARSS)—0
Integrating Scene Text and Visual Appearance for Fine-Grained Image Classification—0
Interpretable Attention Guided Network for Fine-grained Visual Classification—0
Automatic Fine-grained Glomerular Lesion Recognition in Kidney Pathology—0
Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions—0
A Unified Framework to Analyze and Design the Nonlocal Blocks for Neural Networks—0
Attribute Mix: Semantic Data Augmentation for Fine Grained Recognition—0
Knowledge-Embedded Representation Learning for Fine-Grained Image Recognition—0
Fine-Tuning DARTS for Image Classification—0
Assessing The Importance Of Colours For CNNs In Object Recognition—0
ACE: Adaptive Confusion Energy for Natural World Data Distribution—0
Large Neural Networks Learning from Scratch with Very Few Data and without Explicit Regularization—0
Fine-Grained Visual Classification with Batch Confusion Norm—0
Fine-Grained Visual Classification with Efficient End-to-end Localization—0
Leaf Cultivar Identification via Prototype-enhanced Learning—0
Fine-Grained Visual Classification of Aircraft—0
A Spectral Nonlocal Block for Neural Networks—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