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 4801–4825 of 10420 papers

TitleStatusHype
Feature-level augmentation to improve robustness of deep neural networks to affine transformations—0
Multi-relation Message Passing for Multi-label Text ClassificationCode0
Spherical Transformer—0
Class Distance Weighted Cross-Entropy Loss for Ulcerative Colitis Severity EstimationCode1
L2B: Learning to Bootstrap Robust Models for Combating Label NoiseCode1
Improving greedy core-set configurations for active learning with uncertainty-scaled distances—0
Image Difference Captioning with Pre-training and Contrastive LearningCode1
Methods for the frugal labeler: Multi-class semantic segmentation on heterogeneous labelsCode0
TransformNet: Self-supervised representation learning through predicting geometric transformationsCode0
Comparative Study Between Distance Measures On Supervised Optimum-Path Forest ClassificationCode0
Equivariance versus Augmentation for Spherical ImagesCode0
If a Human Can See It, So Should Your System: Reliability Requirements for Machine Vision Components—0
Data Consistency for Weakly Supervised Learning—0
DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation ModelsCode3
Uncertainty Modeling for Out-of-Distribution GeneralizationCode1
Multi-Label Classification of Thoracic Diseases using Dense Convolutional Network on Chest RadiographsCode0
Modeling Structure with Undirected Neural NetworksCode0
Simple Control Baselines for Evaluating Transfer Learning—0
Diversify and Disambiguate: Learning From Underspecified DataCode1
Dataset Condensation with Contrastive SignalsCode1
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and LanguageCode1
OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning FrameworkCode0
Transformers in Self-Supervised Monocular Depth Estimation with Unknown Camera IntrinsicsCode1
Corrupted Image Modeling for Self-Supervised Visual Pre-Training—0
No Parameters Left Behind: Sensitivity Guided Adaptive Learning Rate for Training Large Transformer ModelsCode1
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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