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 7651–7675 of 10420 papers

TitleStatusHype
FLVoogd: Robust And Privacy Preserving Federated Learning—0
A Spectral Nonlocal Block for Neural Networks—0
Pseudo Labels for Single Positive Multi-Label Learning—0
FLuRKA: Fast and accurate unified Low-Rank & Kernel Attention—0
Detached Error Feedback for Distributed SGD with Random Sparsification—0
Pseudo Rehearsal using non photo-realistic images—0
Considerations for a PAP Smear Image Analysis System with CNN Features—0
Safe Semi-Supervised Contrastive Learning Using In-Distribution Data as Positive Examples—0
Flow-Mixup: Classifying Multi-labeled Medical Images with Corrupted Labels—0
Exploration of Noise Strategies in Semi-supervised Named Entity Classification—0
PSO-PS: Parameter Synchronization with Particle Swarm Optimization for Distributed Training of Deep Neural Networks—0
Explore the Effect of Data Selection on Poison Efficiency in Backdoor Attacks—0
Florida Wildlife Camera Trap Dataset—0
PUDD: Towards Robust Multi-modal Prototype-based Deepfake Detection—0
Pulmonary embolism identification in computerized tomography pulmonary angiography scans with deep learning technologies in COVID-19 patients—0
Explore the Power of Dropout on Few-shot Learning—0
Consensus Clustering With Unsupervised Representation Learning—0
Purification Of Contaminated Convolutional Neural Networks Via Robust Recovery: An Approach with Theoretical Guarantee in One-Hidden-Layer Case—0
Pushing Boundaries: Exploring Zero Shot Object Classification with Large Multimodal Models—0
Pushing Joint Image Denoising and Classification to the Edge—0
FLOPs as a Direct Optimization Objective for Learning Sparse Neural Networks—0
Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point—0
Pushing the Limits of Radiology with Joint Modeling of Visual and Textual Information—0
Exploring Category-correlated Feature for Few-shot Image Classification—0
Connectivity Learning in Multi-Branch Networks—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
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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified