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 9801–9825 of 10420 papers

TitleStatusHype
Improving CNN classifiers by estimating test-time priors—0
Improving Deep Learning through Automatic Programming—0
Improving Deep Neural Networks with Probabilistic Maxout Units—0
Improving End-to-End Memory Networks with Unified Weight Tying—0
Improving Explainability of Image Classification in Scenarios with Class Overlap: Application to COVID-19 and Pneumonia—0
Improving Few-Shot Image Classification Using Machine- and User-Generated Natural Language Descriptions—0
Enhancing Few-Shot Image Classification with Unlabelled Examples—0
Improving Few-Shot Visual Classification with Unlabelled Examples—0
Improving Data-Efficient Fossil Segmentation via Model Editing—0
Improving Global Adversarial Robustness Generalization With Adversarially Trained GAN—0
Improving greedy core-set configurations for active learning with uncertainty-scaled distances—0
Improving High Resolution Histology Image Classification with Deep Spatial Fusion Network—0
Improving Human-AI Collaboration With Descriptions of AI Behavior—0
Improving Hyperbolic Representations via Gromov-Wasserstein Regularization—0
Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach—0
Improving Image Classification Robustness through Selective CNN-Filters Fine-Tuning—0
Improving Image Classification with Location Context—0
Improving Image Recognition by Retrieving from Web-Scale Image-Text Data—0
Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters—0
Improving Label Error Detection and Elimination with Uncertainty Quantification—0
Improving Layer-wise Adaptive Rate Methods using Trust Ratio Clipping—0
Improving Machine Reading Comprehension via Adversarial Training—0
Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation—0
Improving Model Accuracy for Imbalanced Image Classification Tasks by Adding a Final Batch Normalization Layer: An Empirical Study—0
Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation—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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified