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 5801–5850 of 10420 papers

TitleStatusHype
Weakly Supervised Patch Label Inference Networks for Efficient Pavement Distress Detection and Recognition in the WildCode0
Efficient Maximal Coding Rate Reduction by Variational Forms—0
Self-distillation Augmented Masked Autoencoders for Histopathological Image Classification—0
Conditional Autoregressors are Interpretable Classifiers—0
SIT: A Bionic and Non-Linear Neuron for Spiking Neural Network—0
AmsterTime: A Visual Place Recognition Benchmark Dataset for Severe Domain ShiftCode0
Treatment Learning Causal Transformer for Noisy Image Classification—0
Classification of NEQR Processed Classical Images using Quantum Neural Networks (QNN)—0
4Weed Dataset: Annotated Imagery Weeds Dataset—0
Edge Detection and Deep Learning Based SETI Signal Classification Method—0
A Fast and Efficient Conditional Learning for Tunable Trade-Off between Accuracy and Robustness—0
Neurosymbolic hybrid approach to driver collision warning—0
WSEBP: A Novel Width-depth Synchronous Extension-based Basis Pursuit Algorithm for Multi-Layer Convolutional Sparse CodingCode0
Image quality assessment for machine learning tasks using meta-reinforcement learning—0
On the Neural Tangent Kernel Analysis of Randomly Pruned Neural Networks—0
On the Viability of Monocular Depth Pre-training for Semantic SegmentationCode0
Give Me Your Attention: Dot-Product Attention Considered Harmful for Adversarial Patch Robustness—0
The Fixed Sub-Center: A Better Way to Capture Data Complexity—0
NPC: Neuron Path Coverage via Characterizing Decision Logic of Deep Neural Networks—0
Transformers Meet Visual Learning Understanding: A Comprehensive Review—0
A DNN Optimizer that Improves over AdaBelief by Suppression of the Adaptive Stepsize RangeCode0
A Two-Stage Federated Transfer Learning Framework in Medical Images Classification on Limited Data: A COVID-19 Case Study—0
Locally Asynchronous Stochastic Gradient Descent for Decentralised Deep Learning—0
Efficient Fully Distributed Federated Learning with Adaptive Local Links—0
Multidimensional Belief Quantification for Label-Efficient Meta-LearningCode0
U-Boost NAS: Utilization-Boosted Differentiable Neural Architecture SearchCode0
Robust Classification using Contractive Hamiltonian Neural ODEsCode0
Semi-Targeted Model Poisoning Attack on Federated Learning via Backward Error AnalysisCode0
Feature Distribution Matching for Federated Domain GeneralizationCode0
Generative Modeling Helps Weak Supervision (and Vice Versa)Code0
Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices—0
MTBF-33: A multi-temporal building footprint dataset for 33 counties in the United States (1900-2015)—0
Image Classification on Accelerated Neural Networks—0
Towards Self-Supervised Gaze Estimation—0
Training Quantised Neural Networks with STE Variants: the Additive Noise Annealing Algorithm—0
Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward—0
Vision Transformer with Convolutions Architecture Search—0
Over-parameterization: A Necessary Condition for Models that Extrapolate—0
CRISPnet: Color Rendition ISP Net—0
Deep Learning Generalization, Extrapolation, and Over-parameterization—0
Incremental Few-Shot Learning via Implanting and Compressing—0
Three things everyone should know about Vision TransformersCode0
Identifying Transients in the Dark Energy Survey using Convolutional Neural NetworksCode0
Transframer: Arbitrary Frame Prediction with Generative Models—0
An Interactive Explanatory AI System for Industrial Quality Control—0
Confidence Dimension for Deep Learning based on Hoeffding Inequality and Relative Evaluation—0
A Continual Learning Framework for Adaptive Defect Classification and Inspection—0
A New Quantum CNN Model for Image Classification—0
Meta-Learning of NAS for Few-shot Learning in Medical Image Applications—0
Learning to Generate Synthetic Training Data using Gradient Matching and Implicit DifferentiationCode0
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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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified