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 4651–4700 of 10420 papers

TitleStatusHype
Federated Deep Learning with Bayesian Privacy—0
Federated Active Learning (F-AL): an Efficient Annotation Strategy for Federated Learning—0
Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition under Occlusion—0
Combining Compositional Models and Deep Networks For Robust Object Classification under Occlusion—0
FedDropoutAvg: Generalizable federated learning for histopathology image classification—0
Compositional Attribute Imbalance in Vision Datasets—0
FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning—0
FedDiff: Diffusion Model Driven Federated Learning for Multi-Modal and Multi-Clients—0
Composite Binary Decomposition Networks—0
Art Forgery Detection using Kolmogorov Arnold and Convolutional Neural Networks—0
CompNet: A Designated Model to Handle Combinations of Images and Designed features—0
FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout—0
Complex-valued reservoir computing for aspect classification and slope-angle estimation with low computational cost and high resolution in interferometric synthetic aperture radar—0
Art Authentication with Vision Transformers—0
A Feasibility study for Deep learning based automated brain tumor segmentation using Magnetic Resonance Images—0
Acoustic Signal Analysis with Deep Neural Network for Detecting Fault Diagnosis in Industrial Machines—0
A Base Model Selection Methodology for Efficient Fine-Tuning—0
FedBABU: Toward Enhanced Representation for Federated Image Classification—0
Complex Scene Classification of PolSAR Imagery based on a Self-paced Learning Approach—0
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning—0
Feature Whitening via Gradient Transformation for Improved Convergence—0
ComplexFace: a Multi-Representation Approach for Image Classification with Small Dataset—0
Art-Attack: Black-Box Adversarial Attack via Evolutionary Art—0
Feature Weaken: Vicinal Data Augmentation for Classification—0
Feature Space Augmentation for Long-Tailed Data—0
Feature selection of neural networks is skewed towards the less abstract cue—0
A fast dynamic graph convolutional network and CNN parallel network for hyperspectral image classification—0
Features based Mammogram Image Classification using Weighted Feature Support Vector Machine—0
Feature Representation in Convolutional Neural Networks—0
Competitive Multi-scale Convolution—0
Feature Preserving Shrinkage on Bayesian Neural Networks via the R2D2 Prior—0
Competition-based Adaptive ReLU for Deep Neural Networks—0
Learning scale-variant features for robust iris authentication with deep learning based ensemble framework—0
Feature Map Convergence Evaluation for Functional Module—0
Feature Losses for Adversarial Robustness—0
Feature-level augmentation to improve robustness of deep neural networks to affine transformations—0
Competing Ratio Loss for Discriminative Multi-class Image Classification—0
A Fast Approximate AIB Algorithm for Distributional Word Clustering—0
Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning—0
Feature Learning to Automatically Assess Radiographic Knee Osteoarthritis Severity—0
Feature Learning beyond the Lazy-Rich Dichotomy: Insights from Representational Geometry—0
Competing Mutual Information Constraints with Stochastic Competition-based Activations for Learning Diversified Representations—0
Feature Kernel Distillation—0
Compensation Learning—0
A Robust and Low Complexity Deep Learning Model for Remote Sensing Image Classification—0
CORL: Compositional Representation Learning for Few-Shot Classification—0
Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering—0
Comparison of Update and Genetic Training Algorithms in a Memristor Crossbar Perceptron—0
A Robust Adversarial Ensemble with Causal (Feature Interaction) Interpretations for Image Classification—0
A Fast and Precise Method for Large-Scale Land-Use Mapping Based on Deep 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