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 7976–8000 of 10420 papers

TitleStatusHype
Improving Chest X-Ray Classification by RNN-based Patient Monitoring—0
Improving Bag-of-Visual-Words Towards Effective Facial Expressive Image Classification—0
ReSet: Learning Recurrent Dynamic Routing in ResNet-like Neural Networks—0
A Pseudo-labelling Auto-Encoder for unsupervised image classification—0
Deep Boosting: Layered Feature Mining for General Image Classification—0
Improving Adversarially Robust Few-Shot Image Classification With Generalizable Representations—0
Improved Techniques for Adversarial Discriminative Domain Adaptation—0
Deep Boosting: Joint Feature Selection and Analysis Dictionary Learning in Hierarchy—0
ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts—0
Adaptive Classification of Interval-Valued Time Series—0
Residual Error: a New Performance Measure for Adversarial Robustness—0
Deep Axial Hypercomplex Networks—0
Improve Unsupervised Domain Adaptation with Mixup Training—0
Deep Autoencoder Model Construction Based on Pytorch—0
Residual Squeeze VGG16—0
Improvement Strategies for Few-Shot Learning in OCT Image Classification of Rare Retinal Diseases—0
Resisting Adversarial Attacks in Deep Neural Networks using Diverse Decision Boundaries—0
ResizeMix: Mixing Data with Preserved Object Information and True Labels—0
Improvement of image classification by multiple optical scattering—0
Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities—0
Automatic Radiology Report Generation based on Multi-view Image Fusion and Medical Concept Enrichment—0
Identify ambiguous tasks combining crowdsourced labels by weighting Areas Under the Margin—0
Improved Training Speed, Accuracy, and Data Utilization via Loss Function Optimization—0
Deep Attributes from Context-Aware Regional Neural Codes—0
Improved Trainable Calibration Method for Neural Networks on Medical Imaging Classification—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
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
10RevCol-HTop 1 Accuracy90—Unverified