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 6351–6400 of 10420 papers

TitleStatusHype
Which Design Decisions in AI-enabled Mobile Applications Contribute to Greener AI?—0
DeepPSL: End-to-end perception and reasoning—0
Multi-Semantic Image Recognition Model and Evaluating Index for explaining the deep learning models—0
Turning old models fashion again: Recycling classical CNN networks using the Lattice Transformation—0
A Strong Baseline for the VIPriors Data-Efficient Image Classification Challenge—0
Federated Deep Learning with Bayesian Privacy—0
Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent PriorsCode0
Predicting Driver Self-Reported Stress by Analyzing the Road Scene—0
Training on Test Data with Bayesian Adaptation for Covariate Shift—0
Audio-to-Image Cross-Modal Generation—0
ST-MAML: A Stochastic-Task based Method for Task-Heterogeneous Meta-Learning—0
Accelerated PDEs for Construction and Theoretical Analysis of an SGD Extension—0
Frequency Disentangled Residual Network—0
Classification of COVID-19 from CXR Images in a 15-class Scenario: an Attempt to Avoid Bias in the System—0
Distribution-sensitive Information Retention for Accurate Binary Neural Network—0
From images in the wild to video-informed image classification—0
Frequency Pooling: Shift-Equivalent and Anti-Aliasing Downsampling—0
A Multi-stage Transfer Learning Framework for Diabetic Retinopathy Grading on Small Data—0
FooBaR: Fault Fooling Backdoor Attack on Neural Network TrainingCode0
Partial sensitivity analysis in differential privacyCode0
Multi-Domain Few-Shot Learning and Dataset for Agricultural Applications—0
Explaining Convolutional Neural Networks by Tagging Filters—0
Audio-Visual Speech Recognition is Worth 32328 Voxels—0
GhostShiftAddNet: More Features from Energy-Efficient OperationsCode0
Class incremental learning for video action classification—0
Ontology-based n-ball Concept Embeddings Informing Few-shot Image Classification—0
Splitfed learning without client-side synchronization: Analyzing client-side split network portion size to overall performance—0
The Unreasonable Effectiveness of the Final Batch Normalization Layer—0
Decision Tree Learning with Spatial Modal Logics—0
Transformer-Unet: Raw Image Processing with Unet—0
Exploiting Activation based Gradient Output Sparsity to Accelerate Backpropagation in CNNs—0
Deep Algorithmic Question Answering: Towards a Compositionally Hybrid AI for Algorithmic Reasoning—0
Sign-MAML: Efficient Model-Agnostic Meta-Learning by SignSGDCode0
Partner-Assisted Learning for Few-Shot Image Classification—0
AdaPruner: Adaptive Channel Pruning and Effective Weights Inheritance—0
A trainable monogenic ConvNet layer robust in front of large contrast changes in image classificationCode0
Task Guided Compositional Representation Learning for ZDA—0
PAT: Pseudo-Adversarial Training For Detecting Adversarial Videos—0
Robust Contrastive Active Learning with Feature-guided Query Strategies—0
Fine-Grained Few Shot Learning with Foreground Object Transformation—0
Check Your Other Door! Creating Backdoor Attacks in the Frequency Domain—0
BioLCNet: Reward-modulated Locally Connected Spiking Neural NetworksCode0
On the Initial Behavior Monitoring Issues in Federated Learning—0
Saliency Guided Experience Packing for Replay in Continual LearningCode0
Fair Comparison: Quantifying Variance in Resultsfor Fine-grained Visual Categorization—0
Tom: Leveraging trend of the observed gradients for faster convergenceCode0
Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper)—0
Generatively Augmented Neural Network Watchdog for Image Classification Networks—0
Vision Transformers For Weeds and Crops Classification Of High Resolution UAV Images—0
Rethinking Crowdsourcing Annotation: Partial Annotation with Salient Labels for Multi-Label Image Classification—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