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 9726–9750 of 10420 papers

TitleStatusHype
Learning Hyperparameters via a Data-Emphasized Variational ObjectiveCode0
Non-convex regularization in remote sensingCode0
Better Self-training for Image Classification through Self-supervisionCode0
An Image Patch is a Wave: Phase-Aware Vision MLPCode0
REVEL Framework to measure Local Linear Explanations for black-box models: Deep Learning Image Classification case of studyCode0
Learning Implicitly Recurrent CNNs Through Parameter SharingCode0
Classification with 2-D Convolutional Neural Networks for breast cancer diagnosisCode0
Learning Spatially-Adaptive Squeeze-Excitation Networks for Image Synthesis and Image RecognitionCode0
Noisy Batch Active Learning with Deterministic AnnealingCode0
Angle based dynamic learning rate for gradient descentCode0
Learning in Deep Factor Graphs with Gaussian Belief PropagationCode0
ImageNet Pre-training also Transfers Non-RobustnessCode0
Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization GuaranteesCode0
Learning Interpretable Models Through Multi-Objective Neural Architecture SearchCode0
Preventing Manifold Intrusion with Locality: Local MixupCode0
Deep Competitive Pathway NetworksCode0
PRICURE: Privacy-Preserving Collaborative Inference in a Multi-Party SettingCode0
Better May Not Be Fairer: A Study on Subgroup Discrepancy in Image ClassificationCode0
An experimental comparative study of backpropagation and alternatives for training binary neural networks for image classificationCode0
Learning in Wilson-Cowan model for metapopulationCode0
Deep Combinatorial AggregationCode0
Non-Negative Networks Against Adversarial AttacksCode0
Learning local discrete features in explainable-by-design convolutional neural networksCode0
On Biases in a UK Biobank-based Retinal Image Classification ModelCode0
Learning Longer-term Dependencies in RNNs with Auxiliary LossesCode0
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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