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 6101–6150 of 10420 papers

TitleStatusHype
Learning transformer-based heterogeneously salient graph representation for multimodal remote sensing image classification—0
MADS: Multi-Attribute Document Supervision for Zero-Shot Image Classification—0
Learning to Utilize Correlated Auxiliary Noise: A Possible Quantum Advantage—0
mAedesID: Android Application for Aedes Mosquito Species Identification using Convolutional Neural Network—0
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification—0
A Closed-Form Learned Pooling for Deep Classification Networks—0
Learning to Teach with Dynamic Loss Functions—0
Maintaining Performance with Less Data—0
Detection of Plant Leaf Disease Directly in the JPEG Compressed Domain using Transfer Learning Technique—0
Make A Long Image Short: Adaptive Token Length for Vision Transformers—0
Learning to Reweight with Deep Interactions—0
Learning to Teach—0
Detection of Non-uniformity in Parameters for Magnetic Domain Pattern Generation by Machine Learning—0
Learning to Specialize with Knowledge Distillation for Visual Question Answering—0
Learning and Sharing: A Multitask Genetic Programming Approach to Image Feature Learning—0
Learning to See Physical Properties with Active Sensing Motor Policies—0
Detection of Degraded Acacia tree species using deep neural networks on uav drone imagery—0
Bias-Eliminating Augmentation Learning for Debiased Federated Learning—0
Learning to see across Domains and Modalities—0
Learning to Schedule Learning rate with Graph Neural Networks—0
Detection of concealed cars in complex cargo X-ray imagery using Deep Learning—0
Mako: Semi-supervised continual learning with minimal labeled data via data programming—0
Malaria detection from RBC images using shallow Convolutional Neural Networks—0
Learning to Sample: an Active Learning Framework—0
Detection of Children Abuse by Voice and Audio Classification by Short-Time Fourier Transform Machine Learning implemented on Nvidia Edge GPU device—0
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
Show:102550
← PrevPage 123 of 209Next →
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
10RevCol-HTop 1 Accuracy90—Unverified