SOTAVerified

Image Classification

Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.

Source: Metamorphic Testing for Object Detection Systems

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4701–4725 of 10420 papers

TitleStatusHype
Deep Multimodal Guidance for Medical Image ClassificationCode1
Renyi Fair Information Bottleneck for Image Classification—0
Multiscale Convolutional Transformer with Center Mask Pretraining for Hyperspectral Image Classification—0
Active Self-Semi-Supervised Learning for Few Labeled Samples—0
Uni4Eye: Unified 2D and 3D Self-supervised Pre-training via Masked Image Modeling Transformer for Ophthalmic Image Classification—0
PASS: Part-Aware Self-Supervised Pre-Training for Person Re-IdentificationCode1
ParC-Net: Position Aware Circular Convolution with Merits from ConvNets and TransformerCode2
Dynamic Group Transformer: A General Vision Transformer Backbone with Dynamic Group Attention—0
Graph Attention Transformer Network for Multi-Label Image ClassificationCode1
Selective-Supervised Contrastive Learning with Noisy LabelsCode1
Discriminability-Transferability Trade-Off: An Information-Theoretic PerspectiveCode0
Art-Attack: Black-Box Adversarial Attack via Evolutionary Art—0
Explaining Classifiers by Constructing Familiar ConceptsCode0
Dynamic MLP for Fine-Grained Image Classification by Leveraging Geographical and Temporal InformationCode1
Dynamic ConvNets on Tiny Devices via Nested Sparsity—0
WaveMix: Resource-efficient Token Mixing for ImagesCode1
Graph Neural Networks for Image Classification and Reinforcement Learning using Graph representations—0
Fidelity of Interpretability Methods and Perturbation Artifacts in Neural Networks—0
MetaFormer: A Unified Meta Framework for Fine-Grained RecognitionCode2
Dynamic Backdoors with Global Average Pooling—0
FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis—0
Class-Aware Contrastive Semi-Supervised LearningCode1
DiT: Self-supervised Pre-training for Document Image TransformerCode1
Ensembles of Vision Transformers as a New Paradigm for Automated Classification in EcologyCode0
Semi-supervised Learning using Robust LossCode0
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All datasetsImageNetCIFAR-10CIFAR-100STL-10ObjectNetMNISTSVHNiNaturalist 2018ImageNet ReaLFlowers-102Clothing1Mmini WebVision 1.0

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91—Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98—Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94—Unverified
4DaViT-GTop 1 Accuracy90.4—Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
6DaViT-HTop 1 Accuracy90.2—Unverified
7SwinV2-GTop 1 Accuracy90.17—Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1—Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05—Unverified
10RevCol-HTop 1 Accuracy90—Unverified