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 8751–8800 of 10420 papers

TitleStatusHype
Dynamic Group Transformer: A General Vision Transformer Backbone with Dynamic Group Attention—0
Dynamic Inference with Neural Interpreters—0
Dynamic Memory-enhanced Transformer for Hyperspectral Image Classification—0
Dynamic Neural Network Channel Execution for Efficient Training—0
Dynamic Object Comprehension: A Framework For Evaluating Artificial Visual Perception—0
Dynamic Perturbation-Adaptive Adversarial Training on Medical Image Classification—0
Dynamic Policy-Driven Adaptive Multi-Instance Learning for Whole Slide Image Classification—0
Dynamic Probabilistic Pruning: A general framework for hardware-constrained pruning at different granularities—0
Dynamic Routing on Deep Neural Network for Thoracic Disease Classification and Sensitive Area Localization—0
Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning—0
Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learning—0
Dynamic Shuffle: An Efficient Channel Mixture Method—0
Dynamic Spectrum Mixer for Visual Recognition—0
Dynamic texture and scene classification by transferring deep image features—0
Early Detection and Classification of Breast Cancer Using Deep Learning Techniques—0
Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network—0
Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads—0
Eating Habits Discovery in Egocentric Photo-streams—0
EBHI:A New Enteroscope Biopsy Histopathological H&E Image Dataset for Image Classification Evaluation—0
SCALES: Boost Binary Neural Network for Image Super-Resolution with Efficient Scalings—0
ECG Arrhythmia Classification Using Transfer Learning from 2-Dimensional Deep CNN Features—0
ECO: Ensembling Context Optimization for Vision-Language Models—0
EDDA: Explanation-driven Data Augmentation to Improve Explanation Faithfulness—0
Edge AI: Evaluation of Model Compression Techniques for Convolutional Neural Networks—0
Edge AI without Compromise: Efficient, Versatile and Accurate Neurocomputing in Resistive Random-Access Memory—0
Edge-Cloud Cooperation for DNN Inference via Reinforcement Learning and Supervised Learning—0
Edge Detection and Deep Learning Based SETI Signal Classification Method—0
EEG-based Image Feature Extraction for Visual Classification using Deep Learning—0
EEG-NeXt: A Modernized ConvNet for The Classification of Cognitive Activity from EEG—0
EffCNet: An Efficient CondenseNet for Image Classification on NXP BlueBox—0
Effective and Robust Detection of Adversarial Examples via Benford-Fourier Coefficients—0
Effective Data Augmentation with Multi-Domain Learning GANs—0
Effective Dimension Aware Fractional-Order Stochastic Gradient Descent for Convex Optimization Problems—0
Effective Evaluation of Deep Active Learning on Image Classification Tasks—0
Effective, Fast, and Memory-Efficient Compressed Multi-function Convolutional Neural Networks for More Accurate Medical Image Classification—0
Effective Features of Remote Sensing Image Classification Using Interactive Adaptive Thresholding Method—0
Effective Label Propagation for Discriminative Semi-Supervised Domain Adaptation—0
Effective Mutation Rate Adaptation through Group Elite Selection—0
Effectiveness of Function Matching in Driving Scene Recognition—0
Effective Sequential Classifier Training for SVM-based Multitemporal Remote Sensing Image Classification—0
Effective training of deep convolutional neural networks for hyperspectral image classification through artificial labeling—0
Effective Version Space Reduction for Convolutional Neural Networks—0
Effect of Radiology Report Labeler Quality on Deep Learning Models for Chest X-Ray Interpretation—0
Effects of Auxiliary Knowledge on Continual Learning—0
Effects of Image Degradations to CNN-based Image Classification—0
Efficacy of Pixel-Level OOD Detection for Semantic Segmentation—0
Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network—0
Efficient Adaptation of Pre-trained Vision Transformer underpinned by Approximately Orthogonal Fine-Tuning Strategy—0
Efficient Adaptive Ensembling for Image Classification—0
Efficient and Flexible Method for Reducing Moderate-size Deep Neural Networks with Condensation—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