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 6601–6625 of 10420 papers

TitleStatusHype
Spatial Assembly Networks for Image Representation Learning—0
CapsuleRRT: Relationships-Aware Regression Tracking via Capsules—0
Self-Supervised Wasserstein Pseudo-Labeling for Semi-Supervised Image Classification—0
Distribution-Aware Adaptive Multi-Bit Quantization—0
Teacher's pet: understanding and mitigating biases in distillation—0
MaxUp: Lightweight Adversarial Training With Data Augmentation Improves Neural Network Training—0
Boosting Ensemble Accuracy by Revisiting Ensemble Diversity MetricsCode0
QFCNN: Quantum Fourier Convolutional Neural Network—0
Informative Class Activation Maps—0
Quality-Agnostic Image Recognition via Invertible DecoderCode0
Practical Transferability Estimation for Image Classification Tasks—0
Hilbert Sinkhorn Divergence for Optimal Transport—0
Cross Modality Knowledge Distillation for Multi-Modal Aerial View Object ClassificationCode0
Certified robustness against adversarial patch attacks via randomized cropping—0
Effective Model Sparsification by Scheduled Grow-and-Prune MethodsCode0
Light Lies: Optical Adversarial Attack—0
Defending Adversaries Using Unsupervised Feature Clustering VAE—0
Quantized Neural Networks via -1, +1 Encoding Decomposition and AccelerationCode0
Residual Error: a New Performance Measure for Adversarial Robustness—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 reinforcement learning with automated label extraction from clinical reports accurately classifies 3D MRI brain volumes—0
ShuffleBlock: Shuffle to Regularize Deep Convolutional Neural Networks—0
Robust Training in High Dimensions via Block Coordinate Geometric Median DescentCode0
Structured DropConnect for Uncertainty Inference in Image ClassificationCode0
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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