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 7851–7900 of 10420 papers

TitleStatusHype
Deep Degradation Prior for Low-Quality Image Classification—0
Recurrent Neural Networks to Correct Satellite Image Classification Maps—0
Deep Decision Network for Multi-Class Image Classification—0
Auxiliary Class Based Multiple Choice Learning—0
Alternating Gradient Descent and Mixture-of-Experts for Integrated Multimodal Perception—0
Improving plant disease classification by adaptive minimal ensembling—0
Improving Performance of Semi-Supervised Learning by Adversarial Attacks—0
Deep Curriculum Learning for PolSAR Image Classification—0
Improving Object Detection with Selective Self-supervised Self-training—0
ReDistill: Residual Encoded Distillation for Peak Memory Reduction—0
RedSync : Reducing Synchronization Traffic for Distributed Deep Learning—0
Reduced Jeffries-Matusita distance: A Novel Loss Function to Improve Generalization Performance of Deep Classification Models—0
Deep Co-Space: Sample Mining Across Feature Transformation for Semi-Supervised Learning—0
Improving Normalization with the James-Stein Estimator—0
Auto-view contrastive learning for few-shot image recognition—0
Reducing Implicit Bias in Latent Domain Learning—0
Improving Multi-fidelity Optimization with a Recurring Learning Rate for Hyperparameter Tuning—0
Reducing Textural Bias Improves Robustness of Deep Segmentation Models—0
Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation—0
FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning—0
Improving Model Accuracy for Imbalanced Image Classification Tasks by Adding a Final Batch Normalization Layer: An Empirical Study—0
Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation—0
Redundant Information Neural Estimation—0
Deep Convolutional Spiking Neural Networks for Image Classification—0
Auto-tuning TensorFlow Threading Model for CPU Backend—0
Adaptive Cross-Attention-Driven Spatial-Spectral Graph Convolutional Network for Hyperspectral Image Classification—0
Improving Machine Reading Comprehension via Adversarial Training—0
Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation—0
Improving Layer-wise Adaptive Rate Methods using Trust Ratio Clipping—0
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning—0
Improving Label Error Detection and Elimination with Uncertainty Quantification—0
Deep Convolutional Neural Networks as Generic Feature Extractors—0
Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters—0
Deep convolutional networks for pancreas segmentation in CT imaging—0
AutoSampling: Search for Effective Data Sampling Schedules—0
Improving Image Recognition by Retrieving from Web-Scale Image-Text Data—0
Improving Image Classification with Location Context—0
Improving Image Classification Robustness through Selective CNN-Filters Fine-Tuning—0
Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach—0
Deep Convolutional Decision Jungle for Image Classification—0
Improving Hyperbolic Representations via Gromov-Wasserstein Regularization—0
Improving Human-AI Collaboration With Descriptions of AI Behavior—0
Improving High Resolution Histology Image Classification with Deep Spatial Fusion Network—0
Deep Context-Aware Kernel Networks—0
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—0
A Low Rank Promoting Prior for Unsupervised Contrastive Learning—0
Improving greedy core-set configurations for active learning with uncertainty-scaled distances—0
Improving Global Adversarial Robustness Generalization With Adversarially Trained GAN—0
Autonomous Deep Learning: A Genetic DCNN Designer for Image Classification—0
Improving Data-Efficient Fossil Segmentation via Model Editing—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