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 6126–6150 of 10420 papers

TitleStatusHype
Mamba base PKD for efficient knowledge compression—0
Learning to Rank for Active Learning: A Listwise Approach—0
Learning to predict visual brain activity by predicting future sensory states—0
Detection Booster Training: A detection booster training method for improving the accuracy of classifiers.—0
Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning—0
BFO Meets HOG: Feature Extraction Based on Histograms of Oriented p.d.f. Gradients for Image Classification—0
Learning to Name Classes for Vision and Language Models—0
Manifold-based Test Generation for Image Classifiers—0
Detecting Visually Relevant Sentences for Fine-Grained Classification—0
Learning to Model the Tail—0
Manifold Graph with Learned Prototypes for Semi-Supervised Image Classification—0
BFBox: Searching Face-Appropriate Backbone and Feature Pyramid Network for Face Detector—0
Manifold regularization based on Nyström type subsampling—0
Learning to Learn Semantic Factors in Heterogeneous Image Classification—0
Detecting Spurious Correlations via Robust Visual Concepts in Real and AI-Generated Image Classification—0
Learning to Learn Image Classifiers with Visual Analogy—0
Mapping Generative Models onto a Network of Digital Spiking Neurons—0
Be Your Own Best Competitor! Multi-Branched Adversarial Knowledge Transfer—0
An Analysis of the Expressiveness of Deep Neural Network Architectures Based on Their Lipschitz Constants—0
Learning to Learn: How to Continuously Teach Humans and Machines—0
Learning-to-learn enables rapid learning with phase-change memory-based in-memory computing—0
Detecting Overfitting via Adversarial Examples—0
Learning to generate imaginary tasks for improving generalization in meta-learning—0
MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification—0
Detecting Novelties with Empty Classes—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