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 7901–7925 of 10420 papers

TitleStatusHype
Reinforcement Explanation Learning—0
Reinforcement Learning Approach to Active Learning for Image Classification—0
Improving High Resolution Histology Image Classification with Deep Spatial Fusion Network—0
Deep Context-Aware Kernel Networks—0
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—0
Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition—0
Reinforcing Medical Image Classifier to Improve Generalization on Small Datasets—0
A Low Rank Promoting Prior for Unsupervised Contrastive Learning—0
Improving greedy core-set configurations for active learning with uncertainty-scaled distances—0
Relating Regularization and Generalization through the Intrinsic Dimension of Activations—0
Improving Global Adversarial Robustness Generalization With Adversarially Trained GAN—0
Autonomous Deep Learning: A Genetic DCNN Designer for Image Classification—0
Improving Data-Efficient Fossil Segmentation via Model Editing—0
A Low-Cost Neural ODE with Depthwise Separable Convolution for Edge Domain Adaptation on FPGAs—0
Improving Few-Shot Visual Classification with Unlabelled Examples—0
Enhancing Few-Shot Image Classification with Unlabelled Examples—0
Deep Collective Learning: Learning Optimal Inputs and Weights Jointly in Deep Neural Networks—0
Improving Few-Shot Image Classification Using Machine- and User-Generated Natural Language Descriptions—0
Deep Collaborative Learning for Visual Recognition—0
Relaxing Equivariance Constraints with Non-stationary Continuous Filters—0
Relearning Forgotten Knowledge: on Forgetting, Overfit and Training-Free Ensembles of DNNs—0
Relevance Prediction from Eye-movements Using Semi-interpretable Convolutional Neural Networks—0
Improving Explainability of Image Classification in Scenarios with Class Overlap: Application to COVID-19 and Pneumonia—0
Improving End-to-End Memory Networks with Unified Weight Tying—0
Deep CNNs for Peripheral Blood Cell Classification—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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified