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 9376–9400 of 10420 papers

TitleStatusHype
Generalized BackPropagation, Étude De Cas: Orthogonality—0
Generalized Boosting—0
Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness—0
Generalized Convolutional Forest Networks for Domain Generalization and Visual Recognition—0
Generalized Correspondence Matching via Flexible Hierarchical Refinement and Patch Descriptor Distillation—0
Generalized Coverage for More Robust Low-Budget Active Learning—0
Generalized Max Pooling—0
Generalized Resubstitution for Classification Error Estimation—0
Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification—0
A Systematic Review of Generalization Research in Medical Image Classification—0
PhysGAN: Generating Physical-World-Resilient Adversarial Examples for Autonomous Driving—0
Generating Adversarial Perturbation with Root Mean Square Gradient—0
Generating and Modifying Natural Language Explanations—0
Generating Counterfactual Explanations with Natural Language—0
Generating Efficient DNN-Ensembles with Evolutionary Computation—0
Generating Image Captions in Arabic using Root-Word Based Recurrent Neural Networks and Deep Neural Networks—0
Generating Minimal Adversarial Perturbations with Integrated Adaptive Gradients—0
Generating Realistic COVID19 X-rays with a Mean Teacher + Transfer Learning GAN—0
Generating Triples with Adversarial Networks for Scene Graph Construction—0
Generative Adversarial Networks and Probabilistic Graph Models for Hyperspectral Image Classification—0
Generative Adversarial Networks and Conditional Random Fields for Hyperspectral Image Classification—0
Generative Adversarial Networks Based on Transformer Encoder and Convolution Block for Hyperspectral Image Classification—0
Generative Adversarial Networks Based on Collaborative Learning and Attention Mechanism for Hyperspectral Image Classification—0
Generative Adversarial Networks for Bitcoin Data Augmentation—0
Generative Adversarial Zero-shot Learning via Knowledge Graphs—0
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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
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 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