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 5151–5200 of 10420 papers

TitleStatusHype
Full-attention based Neural Architecture Search using Context Auto-regression—0
Nonlinear Tensor Ring Network—0
Probabilistic Contrastive Learning for Domain AdaptationCode1
Masked Autoencoders Are Scalable Vision LearnersCode1
Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classification—0
Keys to Accurate Feature Extraction Using Residual Spiking Neural NetworksCode1
FILIP: Fine-grained Interactive Language-Image Pre-TrainingCode1
Sliced Recursive TransformerCode1
TAGLETS: A System for Automatic Semi-Supervised Learning with Auxiliary DataCode1
Hybrid BYOL-ViT: Efficient approach to deal with small datasets—0
A Relational Model for One-Shot Classification—0
Improved Regularization and Robustness for Fine-tuning in Neural NetworksCode1
Multi-Fake Evolutionary Generative Adversarial Networks for Imbalance Hyperspectral Image Classification—0
Crowdsourcing with Meta-Workers: A New Way to Save the Budget—0
Learning of Time-Frequency Attention Mechanism for Automatic Modulation Recognition—0
First steps on Gamification of Lung Fluid Cells Annotations in the Flower Domain—0
FAST: Faster Arbitrarily-Shaped Text Detector with Minimalist Kernel RepresentationCode1
Virus-MNIST: Machine Learning Baseline Calculations for Image Classification—0
Intrusion Detection: Machine Learning Baseline Calculations for Image Classification—0
Fitness Landscape Footprint: A Framework to Compare Neural Architecture Search Problems—0
Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities—0
Data-Efficient Language Shaped Few-shot Image ClassificationCode0
Arch-Net: Model Distillation for Architecture Agnostic Model DeploymentCode1
Multi-Scale High-Resolution Vision Transformer for Semantic SegmentationCode1
Combating Noise: Semi-supervised Learning by Region Uncertainty Quantification—0
When Does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?Code1
Hierarchical Image Classification with A Literally Toy Dataset—0
Revealing and Protecting Labels in Distributed TrainingCode0
TorchXRayVision: A library of chest X-ray datasets and modelsCode2
Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation—0
Approximation properties of Residual Neural Networks for Kolmogorov PDEs—0
Dynamic Differential-Privacy Preserving SGD—0
RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions—0
MFNet: Multi-class Few-shot Segmentation Network with Pixel-wise Metric Learning—0
UDIS: Unsupervised Discovery of Bias in Deep Visual Recognition ModelsCode0
Domain Agnostic Few-Shot Learning For Document Intelligence—0
Training Integrable Parameterizations of Deep Neural Networks in the Infinite-Width LimitCode0
Scalable Unidirectional Pareto Optimality for Multi-Task Learning with Constraints—0
Explaining Latent Representations with a Corpus of ExamplesCode1
MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep LearningCode1
Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian SpectrumsCode0
Towards artificial general intelligence via a multimodal foundation modelCode1
MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationCode2
Diversity Matters When Learning From Ensembles—0
Physically Explainable CNN for SAR Image ClassificationCode1
Stable Anderson Acceleration for Deep LearningCode1
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers—0
On sensitivity of meta-learning to support dataCode1
Can't Fool Me: Adversarially Robust Transformer for Video Understanding—0
Deep Integrated Pipeline of Segmentation Guided Classification of Breast Cancer from Ultrasound Images—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