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 7151–7200 of 10420 papers

TitleStatusHype
Fusing CNNs and statistical indicators to improve image classificationCode0
Multi-Label Noise Robust Collaborative Learning for Remote Sensing Image ClassificationCode0
Augmentation Inside the Network—0
Minimax Active Learning—0
RAILS: A Robust Adversarial Immune-inspired Learning System—0
Separation and Concentration in Deep NetworksCode0
Enabling Retrain-free Deep Neural Network Pruning using Surrogate Lagrangian Relaxation—0
Learning and Sharing: A Multitask Genetic Programming Approach to Image Feature Learning—0
Attention-based Image Upsampling—0
Difficulty in estimating visual information from randomly sampled images—0
Deep Learning of Cell Classification using Microscope Images of Intracellular Microtubule Networks—0
mDALU: Multi-Source Domain Adaptation and Label Unification with Partial Datasets—0
CosSGD: Communication-Efficient Federated Learning with a Simple Cosine-Based Quantization—0
Convolutional Neural Networks from Image Markers—0
Combining Similarity and Adversarial Learning to Generate Visual Explanation: Application to Medical Image Classification—0
Application of the Neural Network Dependability Kit in Real-World Environments—0
DSM Refinement with Deep Encoder-Decoder Networks—0
Improving model calibration with accuracy versus uncertainty optimizationCode0
Graphs for deep learning representations—0
Aggregative Self-Supervised Feature Learning from a Limited Sample—0
Privacy-preserving Decentralized Aggregation for Federated Learning—0
Delay Differential Neural Networks—0
Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints—0
Assessing The Importance Of Colours For CNNs In Object Recognition—0
Dependency Decomposition and a Reject Option for Explainable Models—0
Avoiding The Double Descent Phenomenon of Random Feature Models Using Hybrid RegularizationCode0
ALReLU: A different approach on Leaky ReLU activation function to improve Neural Networks PerformanceCode0
Cyclic orthogonal convolutions for long-range integration of features—0
Confidence Estimation via Auxiliary Models—0
Comparison of Update and Genetic Training Algorithms in a Memristor Crossbar Perceptron—0
Distant Domain Transfer Learning for Medical Imaging—0
Investigating Bias in Image Classification using Model Explanations—0
Tensor Composition Net for Visual Relationship Prediction—0
One-Vote Veto: Semi-Supervised Learning for Low-Shot Glaucoma Diagnosis—0
Semi-supervised Active Learning for Instance Segmentation via Scoring Predictions—0
Convolutional Neural Networks for Multispectral Image Cloud Masking—0
Reinforcement Based Learning on Classification Task Could Yield Better Generalization and Adversarial Accuracy—0
Locally optimal detection of stochastic targeted universal adversarial perturbations—0
Multi-temporal and multi-source remote sensing image classification by nonlinear relative normalization—0
A New Window Loss Function for Bone Fracture Detection and Localization in X-ray Images with Point-based Annotation—0
Model Compression Using Optimal Transport—0
DiffPrune: Neural Network Pruning with Deterministic Approximate Binary Gates and L_0 RegularizationCode0
Deformable Gabor Feature Networks for Biomedical Image Classification—0
Randomized kernels for large scale Earth observation applications—0
A Pseudo-labelling Auto-Encoder for unsupervised image classification—0
Using Machine Learning to Automate Mammogram Images Analysis—0
Food Classification with Convolutional Neural Networks and Multi-Class Linear Discernment AnalysisCode0
MyFood: A Food Segmentation and Classification System to Aid Nutritional Monitoring—0
Multi-head Knowledge Distillation for Model Compression—0
Reciprocal Supervised Learning Improves Neural Machine TranslationCode0
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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