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 5776–5800 of 10420 papers

TitleStatusHype
Contrastive Learning of Generalized Game RepresentationsCode1
Deep reinforcement learning with automated label extraction from clinical reports accurately classifies 3D MRI brain volumes—0
Evaluating the Robustness of Bayesian Neural Networks Against Different Types of Attacks—0
MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data—0
Deep Subdomain Adaptation Network for Image ClassificationCode1
XCiT: Cross-Covariance Image TransformersCode3
Multi-Label Learning from Single Positive LabelsCode1
ShuffleBlock: Shuffle to Regularize Deep Convolutional Neural Networks—0
Effective Evaluation of Deep Active Learning on Image Classification Tasks—0
Federated Semi-supervised Medical Image Classification via Inter-client Relation MatchingCode1
Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective AdaptationCode1
Disentangling Semantic-to-visual Confusion for Zero-shot LearningCode0
DeepSplit: Scalable Verification of Deep Neural Networks via Operator SplittingCode0
Input Invex Neural NetworkCode0
ParticleAugment: Sampling-Based Data Augmentation—0
Positional Contrastive Learning for Volumetric Medical Image SegmentationCode1
Robust Training in High Dimensions via Block Coordinate Geometric Median DescentCode0
Structured DropConnect for Uncertainty Inference in Image ClassificationCode0
Test Sample Accuracy Scales with Training Sample Density in Neural NetworksCode0
Zero-sample surface defect detection and classification based on semantic feedback neural network—0
Revisiting the Calibration of Modern Neural NetworksCode0
BEiT: BERT Pre-Training of Image TransformersCode2
A Lightweight ReLU-Based Feature Fusion for Aerial Scene Classification—0
SAR Image Classification Based on Spiking Neural Network through Spike-Time Dependent Plasticity and Gradient DescentCode1
Contextualizing Meta-Learning via Learning to DecomposeCode0
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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