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

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Showing 6626–6650 of 10420 papers

TitleStatusHype
Tensor Composition Net for Visual Relationship Prediction—0
Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label NoiseCode1
Investigating Bias in Image Classification using Model Explanations—0
One-Vote Veto: Semi-Supervised Learning for Low-Shot Glaucoma Diagnosis—0
Semi-supervised Active Learning for Instance Segmentation via Scoring Predictions—0
Convolutional Neural Networks for Multispectral Image Cloud Masking—0
Locally optimal detection of stochastic targeted universal adversarial perturbations—0
Multi-Objective Interpolation Training for Robustness to Label NoiseCode1
Reinforcement Based Learning on Classification Task Could Yield Better Generalization and Adversarial Accuracy—0
Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation using Structurally Regularized Deep ClusteringCode1
Multi-temporal and multi-source remote sensing image classification by nonlinear relative normalization—0
A New Window Loss Function for Bone Fracture Detection and Localization in X-ray Images with Point-based Annotation—0
Model Compression Using Optimal Transport—0
DiffPrune: Neural Network Pruning with Deterministic Approximate Binary Gates and L_0 RegularizationCode0
Deformable Gabor Feature Networks for Biomedical Image Classification—0
Randomized kernels for large scale Earth observation applications—0
Using Machine Learning to Automate Mammogram Images Analysis—0
Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image ClassificationCode1
A Pseudo-labelling Auto-Encoder for unsupervised image classification—0
Food Classification with Convolutional Neural Networks and Multi-Class Linear Discernment AnalysisCode0
Multi-head Knowledge Distillation for Model Compression—0
MyFood: A Food Segmentation and Classification System to Aid Nutritional Monitoring—0
Reciprocal Supervised Learning Improves Neural Machine TranslationCode0
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene UnderstandingCode1
Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantificationCode1
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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