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 5376–5400 of 10420 papers

TitleStatusHype
Chain of Thought Prompt Tuning in Vision Language Models—0
A Novel Automated Classification and Segmentation for COVID-19 using 3D CT Scans—0
A Novel Approach using CapsNet and Deep Belief Network for Detection and Identification of Oral Leukopenia—0
Adversarial Attacks and Defences for Skin Cancer Classification—0
A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks—0
An Efficient Pre-processing Method to Eliminate Adversarial Effects—0
Factorized Higher-Order CNNs with an Application to Spatio-Temporal Emotion Estimation—0
Efficient multivariate sequence classification—0
A Novel Approach to Breast Cancer Histopathological Image Classification Using Cross-Colour Space Feature Fusion and Quantum-Classical Stack Ensemble Method—0
Efficient Multi-Model Fusion with Adversarial Complementary Representation Learning—0
Efficient Multilingual Multi-modal Pre-training through Triple Contrastive Loss—0
Cervical Cancer Detection Using Multi-Branch Deep Learning Model—0
Adversarial Attack Against Images Classification based on Generative Adversarial Networks—0
Efficient Model Performance Estimation via Feature Histories—0
Certified robustness against physically-realizable patch attack via randomized cropping—0
Efficient Model-Agnostic Multi-Group Equivariant Networks—0
Efficient Maximal Coding Rate Reduction by Variational Forms—0
Certified robustness against adversarial patch attacks via randomized cropping—0
A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators—0
Efficient Masked Autoencoders with Self-Consistency—0
Efficiently utilizing complex-valued PolSAR image data via a multi-task deep learning framework—0
Efficient Lung Cancer Image Classification and Segmentation Algorithm Based on Improved Swin Transformer—0
Efficient Large Scale Video Classification—0
Certified Defences Against Adversarial Patch Attacks on Semantic Segmentation—0
A Novel ANN Structure for Image Recognition—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
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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified