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 201–250 of 353 papers

TitleStatusHype
Hyper-Class Augmented and Regularized Deep Learning for Fine-Grained Image Classification—0
Improved Robustness of Vision Transformer via PreLayerNorm in Patch Embedding—0
Integrating Scene Text and Visual Appearance for Fine-Grained Image Classification—0
Interpretable Attention Guided Network for Fine-grained Visual Classification—0
Knowledge-Embedded Representation Learning for Fine-Grained Image Recognition—0
Large Neural Networks Learning from Scratch with Very Few Data and without Explicit Regularization—0
Leaf Cultivar Identification via Prototype-enhanced Learning—0
Learning Deep Classifiers Consistent With Fine-Grained Novelty Detection—0
Learning from Web Data: the Benefit of Unsupervised Object Localization—0
Learning Granularity-Aware Convolutional Neural Network for Fine-Grained Visual Classification—0
LLM-based Hierarchical Concept Decomposition for Interpretable Fine-Grained Image Classification—0
Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition—0
Low-Rank Pairwise Alignment Bilinear Network For Few-Shot Fine-Grained Image Classification—0
Maximum-Entropy Fine Grained Classification—0
Maximum-Entropy Fine-Grained Classification—0
Maximum Entropy Regularization and Chinese Text Recognition—0
MedFocusCLIP : Improving few shot classification in medical datasets using pixel wise attention—0
Modelling Local Deep Convolutional Neural Network Features to Improve Fine-Grained Image Classification—0
Multimodal Semantic Transfer from Text to Image. Fine-Grained Image Classification by Distributional Semantics—0
Natural World Distribution via Adaptive Confusion Energy Regularization—0
NDPNet: A novel non-linear data projection network for few-shot fine-grained image classification—0
Nonparametric Part Transfer for Fine-grained Recognition—0
Object-aware Long-short-range Spatial Alignment for Few-Shot Fine-Grained Image Classification—0
Object-centric Sampling for Fine-grained Image Classification—0
OmniVec2 - A Novel Transformer based Network for Large Scale Multimodal and Multitask Learning—0
OmniVec: Learning robust representations with cross modal sharing—0
On the Ideal Number of Groups for Isometric Gradient Propagation—0
Part-based R-CNNs for Fine-grained Category Detection—0
Part-Stacked CNN for Fine-Grained Visual Categorization—0
Pay Attention to Convolution Filters: Towards Fast and Accurate Fine-Grained Transfer Learning—0
Performing Image Classification for 10 Different Monkey Species using CNN—0
Progressive Multi-stage Interactive Training in Mobile Network for Fine-grained Recognition—0
PVP: Pre-trained Visual Parameter-Efficient Tuning—0
RAMS-Trans: Recurrent Attention Multi-scale Transformer forFine-grained Image Recognition—0
ReDro: Efficiently Learning Large-sized SPD Visual Representation—0
Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition—0
Re-rank Coarse Classification with Local Region Enhanced Features for Fine-Grained Image Recognition—0
Rethinking Generative Zero-Shot Learning: An Ensemble Learning Perspective for Recognising Visual Patches—0
Rethinking Hard-Parameter Sharing in Multi-Domain Learning—0
Robust and Explainable Fine-Grained Visual Classification with Transfer Learning: A Dual-Carriageway Framework—0
RP2K: A Large-Scale Retail Product Dataset for Fine-Grained Image Classification—0
Semantic Feature Integration network for Fine-grained Visual Classification—0
SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation—0
Siamese Networks: The Tale of Two Manifolds—0
SIM-OFE: Structure Information Mining and Object-aware Feature Enhancement for Fine-Grained Visual Categorization—0
Spatial-Aware Non-Local Attention for Fashion Landmark Detection—0
Stochastic Subsampling With Average Pooling—0
Streaming Self-Training via Domain-Agnostic Unlabeled Images—0
Structural feature enhanced transformer for fine-grained image recognition—0
Taxonomy-Aware Evaluation of Vision-Language Models—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
7SWAG (ViT H/14)Accuracy91.7—Unverified
8ViT-NeTAccuracy91.7—Unverified
9TransFGAccuracy91.7—Unverified
10I2-HOFIAccuracy91.6—Unverified
#ModelMetricClaimedVerifiedStatus
1HERBSAccuracy93—Unverified
2MetaFormer (MetaFormer-2,384)Accuracy93—Unverified
3PIMAccuracy92.8—Unverified
4MPSAAccuracy92.5—Unverified
5ViT-NeT (SwinV2-B)Accuracy92.5—Unverified
6CSQA-NetAccuracy92.3—Unverified
7I2-HOFIAccuracy92.12—Unverified
8MDCMAccuracy92—Unverified
9CGLAccuracy91.7—Unverified
10SR-GNNAccuracy91.2—Unverified