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 301350 of 353 papers

TitleStatusHype
Correcting the Triplet Selection Bias for Triplet LossCode0
Grassmann Pooling as Compact Homogeneous Bilinear Pooling for Fine-Grained Visual Classification0
Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic EmbeddingCode0
Weakly Supervised Bilinear Attention Network for Fine-Grained Visual Classification0
Knowledge-Embedded Representation Learning for Fine-Grained Image Recognition0
Generating Counterfactual Explanations with Natural Language0
Large Scale Fine-Grained Categorization and Domain-Specific Transfer LearningCode1
Webly Supervised Learning Meets Zero-Shot Learning: A Hybrid Approach for Fine-Grained Classification0
AutoAugment: Learning Augmentation Policies from DataCode3
Do Better ImageNet Models Transfer Better?0
Unsupervised Learning using Pretrained CNN and Associative Memory Bank0
Aligned to the Object, not to the Image: A Unified Pose-aligned Representation for Fine-grained Recognition0
Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root NormalizationCode0
Learning Multi-Attention Convolutional Neural Network for Fine-Grained Image RecognitionCode0
Fast Fine-grained Image Classification via Weakly Supervised Discriminative Localization0
Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN0
Where to Focus: Deep Attention-based Spatially Recurrent Bilinear Networks for Fine-Grained Visual Recognition0
Zero-Shot Fine-Grained Classification by Deep Feature Learning with Semantics0
Fine-Grained Recognition as HSnet Search for Informative Image Parts0
Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition0
Fine-Grained Image Classification via Combining Vision and Language0
Pairwise Confusion for Fine-Grained Visual ClassificationCode0
Integrating Scene Text and Visual Appearance for Fine-Grained Image Classification0
Fine-graind Image Classification via Combining Vision and Language0
Object-Part Attention Model for Fine-grained Image ClassificationCode0
Deep CNNs With Spatially Weighted Pooling for Fine-Grained Car RecognitionCode0
Convolutional Low-Resolution Fine-Grained Classification0
Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitroCode1
Deep Quantization: Encoding Convolutional Activations with Deep Generative Model0
Gaze Embeddings for Zero-Shot Image Classification0
Generalized BackPropagation, Étude De Cas: Orthogonality0
Detecting Visually Relevant Sentences for Fine-Grained Classification0
Part-Stacked CNN for Fine-Grained Visual Categorization0
Embedding Label Structures for Fine-Grained Feature Representation0
Fine-grained Image Classification by Exploring Bipartite-Graph Labels0
Bilinear CNN Models for Fine-Grained Visual Recognition0
Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks0
The Unreasonable Effectiveness of Noisy Data for Fine-Grained RecognitionCode0
Fine-grained Recognition Datasets for Biodiversity Analysis0
A Large-Scale Car Dataset for Fine-Grained Categorization and VerificationCode0
Hyper-Class Augmented and Regularized Deep Learning for Fine-Grained Image Classification0
Bilinear CNNs for Fine-grained Visual RecognitionCode0
Weakly Supervised Fine-Grained Image Categorization0
Modelling Local Deep Convolutional Neural Network Features to Improve Fine-Grained Image Classification0
Object-centric Sampling for Fine-grained Image Classification0
The Application of Two-level Attention Models in Deep Convolutional Neural Network for Fine-grained Image Classification0
Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors0
Evaluation of Output Embeddings for Fine-Grained Image ClassificationCode0
10,000+ Times Accelerated Robust Subset Selection (ARSS)0
Part-based R-CNNs for Fine-grained Category Detection0
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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