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 4051–4100 of 10420 papers

TitleStatusHype
Shifted Windows Transformers for Medical Image Quality Assessment—0
WeightMom: Learning Sparse Networks using Iterative Momentum-based pruning—0
PatchDropout: Economizing Vision Transformers Using Patch DropoutCode1
Patching open-vocabulary models by interpolating weightsCode1
Machine Learning with DBOS—0
Combining Stochastic Defenses to Resist Gradient Inversion: An Ablation Study—0
SBPF: Sensitiveness Based Pruning Framework For Convolutional Neural Network On Image Classification—0
On the Activation Function Dependence of the Spectral Bias of Neural Networks—0
All-optical image classification through unknown random diffusers using a single-pixel diffractive network—0
No More Strided Convolutions or Pooling: A New CNN Building Block for Low-Resolution Images and Small ObjectsCode2
Multiplex-detection Based Multiple Instance Learning Network for Whole Slide Image Classification—0
A self-interpretable module for deep image classification on small dataCode0
Semi-Supervised Hyperspectral Image Classification Using a Probabilistic Pseudo-Label Generation FrameworkCode1
Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz NetworksCode1
RadTex: Learning Efficient Radiograph Representations from Text Reports—0
Self-Ensembling Vision Transformer (SEViT) for Robust Medical Image ClassificationCode1
Privacy Safe Representation Learning via Frequency Filtering Encoder—0
A Novel Automated Classification and Segmentation for COVID-19 using 3D CT Scans—0
DropKey—0
Semantic Interleaving Global Channel Attention for Multilabel Remote Sensing Image ClassificationCode1
Privacy-Preserving Image Classification Using ConvMixer with Adaptive Permutation Matrix—0
Multiclass ASMA vs Targeted PGD Attack in Image Segmentation—0
SSformer: A Lightweight Transformer for Semantic SegmentationCode1
Maintaining Performance with Less Data—0
Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-LearningCode0
Texture features in medical image analysis: a survey—0
Maximal Independent Vertex Set applied to Graph Pooling—0
Connection Reduction of DenseNet for Image RecognitionCode0
On the Evaluation of User Privacy in Deep Neural Networks using Timing Side Channel—0
giMLPs: Gate with Inhibition Mechanism in MLPsCode0
A Rotation Meanout Network with Invariance for Dermoscopy Image Classification and RetrievalCode0
Information Gain Sampling for Active Learning in Medical Image Classification—0
XOOD: Extreme Value Based Out-Of-Distribution Detection For Image ClassificationCode0
Adaptive Edge Offloading for Image Classification Under Rate LimitCode1
A review of Deep learning Techniques for COVID-19 identification on Chest CT images—0
Class-Difficulty Based Methods for Long-Tailed Visual RecognitionCode1
Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy LabelsCode1
Topological structure of complex predictionsCode1
Pro-tuning: Unified Prompt Tuning for Vision Tasks—0
HorNet: Efficient High-Order Spatial Interactions with Recursive Gated ConvolutionsCode2
Neural Architecture Search on Efficient Transformers and Beyond—0
CrAM: A Compression-Aware MinimizerCode1
On the Effects of Different Types of Label Noise in Multi-Label Remote Sensing Image Classification—0
Text Classification in Memristor-based Spiking Neural NetworksCode1
Image sensing with multilayer, nonlinear optical neural networksCode1
Multi-layer Representation Learning for Robust OOD Image Classification—0
Adaptive sampling for scanning pixel cameras—0
ALBench: A Framework for Evaluating Active Learning in Object DetectionCode2
Contrastive Masked Autoencoders are Stronger Vision LearnersCode1
Generalizable multi-task, multi-domain deep segmentation of sparse pediatric imaging datasets via multi-scale contrastive regularization and multi-joint anatomical priors—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