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 5051–5075 of 10420 papers

TitleStatusHype
A Framework for Generalizing Critical Heat Flux Detection Models Using Unsupervised Image-to-Image Translation—0
Instance-dependent Label Distribution Estimation for Learning with Label NoiseCode0
Test-time Adaptation in the Dynamic World with Compound Domain Knowledge Management—0
Scattering-induced entropy boost for highly-compressed optical sensing and encryption—0
Better May Not Be Fairer: A Study on Subgroup Discrepancy in Image ClassificationCode0
From Xception to NEXcepTion: New Design Decisions and Neural Architecture SearchCode0
Learning to Detect Semantic Boundaries with Image-level Class Labels—0
Bayesian posterior approximation with stochastic ensemblesCode0
Backdoor Attack Detection in Computer Vision by Applying Matrix Factorization on the Weights of Deep Networks—0
CLIPPO: Image-and-Language Understanding from Pixels Only—0
Design-time Fashion Popularity Forecasting in VR Environments—0
SAIF: Sparse Adversarial and Imperceptible Attack Framework—0
Adversarial Attacks and Defences for Skin Cancer Classification—0
CAT: Learning to Collaborate Channel and Spatial Attention from Multi-Information Fusion—0
Can a face tell us anything about an NBA prospect? -- A Deep Learning approach—0
Losses over Labels: Weakly Supervised Learning via Direct Loss ConstructionCode0
A Neural ODE Interpretation of Transformer Layers—0
Synthetic Image Data for Deep Learning—0
Quantum Phase Recognition using Quantum Tensor Networks—0
Using Multiple Instance Learning to Build Multimodal Representations—0
General Adversarial Defense Against Black-box Attacks via Pixel Level and Feature Level Distribution Alignments—0
Analysis of Explainable Artificial Intelligence Methods on Medical Image Classification—0
Algorithmic progress in computer vision—0
Expeditious Saliency-guided Mix-up through Random Gradient ThresholdingCode0
Dual adaptive training of photonic neural 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
10RevCol-HTop 1 Accuracy90—Unverified