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 6551–6600 of 10420 papers

TitleStatusHype
Divergence Regulated Encoder Network for Joint Dimensionality Reduction and ClassificationCode0
Automatic Detection and Image Recognition of Precision Agriculture for Citrus Diseases—0
Black-box Adversarial Attacks on Monocular Depth Estimation Using Evolutionary Multi-objective Optimization—0
MS-GWNN:multi-scale graph wavelet neural network for breast cancer diagnosis—0
Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear MapsCode1
Deep Visual Domain Adaptation—0
Screening COVID-19 Based on CT/CXR Images & Building a Publicly Available CT-scan Dataset of COVID-19—0
Playing to distraction: towards a robust training of CNN classifiers through visual explanation techniquesCode0
WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets for hyperspectral image classification—0
CNNs for JPEGs: A Study in Computational Cost—0
Coarse to Fine: Multi-label Image Classification with Global/Local Attention—0
Direct Quantization for Training Highly Accurate Low Bit-width Deep Neural Networks—0
Mixed-Privacy Forgetting in Deep Networks—0
Learning from Crowds by Modeling Common ConfusionsCode0
Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image ClassificationCode1
Coarse-to-Fine Object Tracking Using Deep Features and Correlation FiltersCode0
Deep Semantic Dictionary Learning for Multi-label Image ClassificationCode1
How Does a Neural Network's Architecture Impact Its Robustness to Noisy Labels?—0
Training data-efficient image transformers & distillation through attentionCode1
A Survey on Visual Transformer—0
General Domain Adaptation Through Proportional Progressive Pseudo LabelingCode0
A Review of Artificial Intelligence Technologies for Early Prediction of Alzheimer's Disease—0
A Second-Order Approach to Learning with Instance-Dependent Label NoiseCode1
A Feasibility study for Deep learning based automated brain tumor segmentation using Magnetic Resonance Images—0
FcaNet: Frequency Channel Attention NetworksCode1
GANterfactual - Counterfactual Explanations for Medical Non-Experts using Generative Adversarial LearningCode1
Differentially Private Synthetic Medical Data Generation using Convolutional GANsCode1
Generative Interventions for Causal LearningCode1
FracBNN: Accurate and FPGA-Efficient Binary Neural Networks with Fractional ActivationsCode1
LQF: Linear Quadratic Fine-Tuning—0
ResizeMix: Mixing Data with Preserved Object Information and True Labels—0
Color Channel Perturbation Attacks for Fooling Convolutional Neural Networks and A Defense Against Such AttacksCode0
Semi-supervised Hyperspectral Image Classification with Graph Clustering Convolutional Networks—0
DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks—0
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
Separation and Concentration in Deep NetworksCode0
When Machine Learning Meets Quantum Computers: A Case StudyCode1
RAILS: A Robust Adversarial Immune-inspired Learning System—0
Enabling Retrain-free Deep Neural Network Pruning using Surrogate Lagrangian Relaxation—0
Image and Text fusion for UPMC Food-101 \ BERT and CNNsCode1
Attention-based Image Upsampling—0
Learning and Sharing: A Multitask Genetic Programming Approach to Image Feature Learning—0
DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic AugmentationCode1
Deep Learning of Cell Classification using Microscope Images of Intracellular Microtubule Networks—0
Difficulty in estimating visual information from randomly sampled images—0
Point TransformerCode1
Convolutional Neural Networks from Image Markers—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