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 7726–7750 of 10420 papers

TitleStatusHype
Quantum-classical convolutional neural networks in radiological image classification—0
Deep Features Analysis with Attention Networks—0
A Vision-based Solution for Track Misalignment Detection—0
A Mathematical Framework for Quantifying Transferability in Multi-source Transfer Learning—0
Quantum-Enhanced Parameter-Efficient Learning for Typhoon Trajectory Forecasting—0
Adaptive Deep Pyramid Matching for Remote Sensing Scene Classification—0
Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Coding—0
Deep Feature Augmentation for Occluded Image Classification—0
Quantum-Inspired Spectral-Spatial Pyramid Network for Hyperspectral Image Classification—0
A Very Compact Embedded CNN Processor Design Based on Logarithmic Computing—0
InceptionCapsule: Inception-Resnet and CapsuleNet with self-attention for medical image Classification—0
In Automation We Trust: Investigating the Role of Uncertainty in Active Learning Systems—0
A Main/Subsidiary Network Framework for Simplifying Binary Neural Networks—0
IMWA: Iterative Model Weight Averaging Benefits Class-Imbalanced Learning Tasks—0
Improving Whole Slide Segmentation Through Visual Context - A Systematic Study—0
Deepfake Video Forensics based on Transfer Learning—0
Quasi-Newton Optimization Methods For Deep Learning Applications—0
Nonlinear Advantage: Trained Networks Might Not Be As Complex as You Think—0
Improving Transferability of Deep Neural Networks—0
Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks—0
Query-Efficient Decision-based Black-Box Patch Attack—0
A Main/Subsidiary Network Framework for Simplifying Binary Neural Network—0
Adaptive Dataset Sampling by Deep Policy Gradient—0
Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How—0
Improving training of deep neural networks via Singular Value Bounding—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