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 7501–7550 of 10420 papers

TitleStatusHype
Predictive Models from Quantum Computer Benchmarks—0
Predictive Subspace Learning for Multi-view Data: a Large Margin Approach—0
Constructing Multiple High-Quality Deep Neural Networks: A TRUST-TECH Based Approach—0
Improving Predictive Uncertainty Estimation using Dropout -- Hamiltonian Monte Carlo—0
predictSLUMS: A new model for identifying and predicting informal settlements and slums in cities from street intersections using machine learning—0
Forgetting Order of Continual Learning: Examples That are Learned First are Forgotten Last—0
A Fusion Model for Art Style and Author Recognition Based on Convolutional Neural Networks and Transformers—0
Forged Image Detection using SOTA Image Classification Deep Learning Methods for Image Forensics with Error Level Analysis—0
ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks—0
Expanding Training Data for Endoscopic Phenotyping of Eosinophilic Esophagitis—0
ForestHash: Semantic Hashing With Shallow Random Forests and Tiny Convolutional Networks—0
Constructing Deep Neural Networks by Bayesian Network Structure Learning—0
Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification—0
Preserve, Promote, or Attack? GNN Explanation via Topology Perturbation—0
Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillation—0
Pre-Trained Convolutional Neural Network Features for Facial Expression Recognition—0
Pre-trained Model Guided Mixture Knowledge Distillation for Adversarial Federated Learning—0
Constraint-Based Regularization of Neural Networks—0
For Better or For Worse? Learning Minimum Variance Features With Label Augmentation—0
Assessing The Importance Of Colours For CNNs In Object Recognition—0
Provably Reliable Conformal Prediction Sets in the Presence of Data Poisoning—0
Pre-training of Lightweight Vision Transformers on Small Datasets with Minimally Scaled Images—0
Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs—0
ClusterViG: Efficient Globally Aware Vision GNNs via Image Partitioning—0
Providing Error Detection for Deep Learning Image Classifiers Using Self-Explainability—0
PRIME: Prioritizing Interpretability in Failure Mode Extraction—0
Constraining Pseudo-label in Self-training Unsupervised Domain Adaptation with Energy-based Model—0
ActiveDC: Distribution Calibration for Active Finetuning—0
Foothill: A Quasiconvex Regularization for Edge Computing of Deep Neural Networks—0
A Computational Account Of Self-Supervised Visual Learning From Egocentric Object Play—0
Fooling Neural Networks for Motion Forecasting via Adversarial Attacks—0
Constrained Low-Rank Learning Using Least Squares-Based Regularization—0
EXPLAINABLE AI-BASED DYNAMIC FILTER PRUNING OF CONVOLUTIONAL NEURAL NETWORKS—0
Privacy-Preserving Collaborative Deep Learning with Unreliable Participants—0
Provably efficient neural network representation for image classification—0
Privacy-preserving Decentralized Aggregation for Federated Learning—0
Explainable AI (XAI) in Image Segmentation in Medicine, Industry, and Beyond: A Survey—0
Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks—0
Privacy-Preserving Federated Learning with Consistency via Knowledge Distillation Using Conditional Generator—0
Privacy-Preserving Image Classification in the Local Setting—0
Privacy-Preserving Image Classification Using Isotropic Network—0
Privacy-Preserving Image Classification Using Vision Transformer—0
Fooling a Real Car with Adversarial Traffic Signs—0
Privacy-preserving Machine Learning for Medical Image Classification—0
A Survey and Evaluation of Adversarial Attacks for Object Detection—0
Privacy-preserving Universal Adversarial Defense for Black-box Models—0
Privacy-Preserving Video Classification with Convolutional Neural Networks—0
Privacy Safe Representation Learning via Frequency Filtering Encoder—0
CNN Based Analysis of the Luria’s Alternating Series Test for Parkinson’s Disease Diagnostics—0
Trade-offs between membership privacy & adversarially robust learning—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