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

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Showing 6301–6350 of 10420 papers

TitleStatusHype
Learning Transferable Visual Models From Natural Language SupervisionCode2
GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical ImagingCode1
Class Knowledge Overlay to Visual Feature Learning for Zero-Shot Image Classification—0
A Universal Model for Cross Modality Mapping by Relational Reasoning—0
Web Table Classification based on Visual Features—0
Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classification—0
Visual Word Embedding for Text ClassificationCode0
Do Input Gradients Highlight Discriminative Features?Code1
Robust Pollen Imagery Classification with Generative Modeling and Mixup Training—0
Transfer Learning with Convolutional Neural Networks for Rainfall Detection in Single ImagesCode0
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without ConvolutionsCode1
Arguments for the Unsuitability of Convolutional Neural Networks for Non--Local Tasks—0
ASAM: Adaptive Sharpness-Aware Minimization for Scale-Invariant Learning of Deep Neural NetworksCode2
FINE Samples for Learning with Noisy LabelsCode1
Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeedCode0
Probabilistic Spatial Analysis in Quantitative Microscopy with Uncertainty-Aware Cell Detection using Deep Bayesian Regression of Density Maps—0
The Uncanny Similarity of Recurrence and DepthCode0
Explainers in the Wild: Making Surrogate Explainers Robust to Distortions through Perception—0
Conditional Positional Encodings for Vision TransformersCode1
MetaDelta: A Meta-Learning System for Few-shot Image ClassificationCode1
Revisiting Classification Perspective on Scene Text Recognition—0
CSIT-Free Model Aggregation for Federated Edge Learning via Reconfigurable Intelligent Surface—0
Combining Spiking Neural Network and Artificial Neural Network for Enhanced Image Classification—0
A Hierarchical Conditional Random Field-based Attention Mechanism Approach for Gastric Histopathology Image Classification—0
Evolving Attention with Residual ConvolutionsCode1
EMDS-5: Environmental Microorganism Image Dataset Fifth Version for Multiple Image Analysis Tasks—0
Neural Sampling Machine with Stochastic Synapse allows Brain-like Learning and Inference—0
Hard-Attention for Scalable Image ClassificationCode1
PRICURE: Privacy-Preserving Collaborative Inference in a Multi-Party SettingCode0
Image Classification using CNN for Traffic Signs in Pakistan—0
One Shot Model For COVID-19 Classification and Lesions Segmentation In Chest CT Scans Using LSTM With Attention MechanismCode0
Center Smoothing: Certified Robustness for Networks with Structured OutputsCode0
Fortify Machine Learning Production Systems: Detect and Classify Adversarial Attacks—0
Adaptive Rational Activations to Boost Deep Reinforcement LearningCode1
Unbiased Teacher for Semi-Supervised Object DetectionCode1
Efficient Online ML API Selection for Multi-Label Classification Tasks—0
Centroid Transformers: Learning to Abstract with Attention—0
LambdaNetworks: Modeling Long-Range Interactions Without AttentionCode2
Robust Domain-Free Domain Generalization with Class-aware Alignment—0
Firefly Neural Architecture Descent: a General Approach for Growing Neural NetworksCode1
DEUP: Direct Epistemic Uncertainty PredictionCode1
Training Stacked Denoising Autoencoders for Representation Learning—0
Just Noticeable Difference for Deep Machine Vision—0
Adaptive Weighting Scheme for Automatic Time-Series Data Augmentation—0
Just Noticeable Difference for Machine Perception and Generation of Regularized Adversarial Images with Minimal Perturbation—0
Instance Localization for Self-supervised Detection PretrainingCode1
AlphaNet: Improved Training of Supernets with Alpha-DivergenceCode1
Dataset Condensation with Differentiable Siamese AugmentationCode0
GradInit: Learning to Initialize Neural Networks for Stable and Efficient TrainingCode1
Momentum Residual Neural NetworksCode1
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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