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
Debiasing Classifiers by Amplifying Bias with Latent Diffusion and Large Language Models—0
Automated identification of hookahs (waterpipes) on Instagram: an application in feature extraction using Convolutional Neural Network and Support Vector Machine classification—0
Access Control of Object Detection Models Using Encrypted Feature Maps—0
Improving Deep Learning through Automatic Programming—0
Improving Evaluation of Debiasing in Image Classification—0
DDS-NAS: Dynamic Data Selection within Neural Architecture Search via On-line Hard Example Mining applied to Image Classification—0
DDIPNet and DDIPNet+: Discriminant Deep Image Prior Networks for Remote Sensing Image Classification—0
Automated eye disease classification method from anterior eye image using anatomical structure focused image classification technique—0
Adaptive Adversarial Attack on Scene Text Recognition—0
Automated COVID-19 CT Image Classification using Multi-head Channel Attention in Deep CNN—0
Accelerated Distributed Stochastic Non-Convex Optimization over Time-Varying Directed Networks—0
DCTNet : A Simple Learning-free Approach for Face Recognition—0
DCT-Mamba3D: Spectral Decorrelation and Spatial-Spectral Feature Extraction for Hyperspectral Image Classification—0
Advancing Ischemic Stroke Diagnosis: A Novel Two-Stage Approach for Blood Clot Origin Identification—0
Improving CNN classifiers by estimating test-time priors—0
Improving Deep Neural Networks with Probabilistic Maxout Units—0
Improving High Resolution Histology Image Classification with Deep Spatial Fusion Network—0
Improving Multi-fidelity Optimization with a Recurring Learning Rate for Hyperparameter Tuning—0
DCT-CompCNN: A Novel Image Classification Network Using JPEG Compressed DCT Coefficients—0
DC-SGD: Differentially Private SGD with Dynamic Clipping through Gradient Norm Distribution Estimation—0
Alleviating the Incompatibility between Cross Entropy Loss and Episode Training for Few-shot Skin Disease Classification—0
DCNN: Dual Cross-current Neural Networks Realized Using An Interactive Deep Learning Discriminator for Fine-grained Objects—0
Alleviating Noisy-label Effects in Image Classification via Probability Transition Matrix—0
Adaptive Active Learning for Image Classification—0
DCAT: Dual Cross-Attention Fusion for Disease Classification in Radiological Images with Uncertainty Estimation—0
CNN depth analysis with different channel inputs for Acoustic Scene Classification—0
Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification—0
Improving Adversarially Robust Few-Shot Image Classification With Generalizable Representations—0
DC-AL GAN: Pseudoprogression and True Tumor Progression of Glioblastoma Multiform Image Classification Based on DCGAN and AlexNet—0
Improved Techniques for Quantizing Deep Networks with Adaptive Bit-Widths—0
Accelerator-aware Neural Network Design using AutoML—0
Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments—0
DaTscan SPECT Image Classification for Parkinson's Disease—0
Autoencoders with Intrinsic Dimension Constraints for Learning Low Dimensional Image Representations—0
Data-Side Efficiencies for Lightweight Convolutional Neural Networks—0
AutoDropout: Learning Dropout Patterns to Regularize Deep Networks—0
A Linear Approximation to the chi^2 Kernel with Geometric Convergence—0
Improved Techniques for Adversarial Discriminative Domain Adaptation—0
A Pseudo-labelling Auto-Encoder for unsupervised image classification—0
Improvement Strategies for Few-Shot Learning in OCT Image Classification of Rare Retinal Diseases—0
Dataset Distillation for Histopathology Image Classification—0
Exploring Localization for Self-supervised Fine-grained Contrastive Learning—0
Adapting to Label Shift with Bias-Corrected Calibration—0
Improve Unsupervised Domain Adaptation with Mixup Training—0
Aligning Human Knowledge with Visual Concepts Towards Explainable Medical Image Classification—0
Dataset Bias Prediction for Few-Shot Image Classification—0
Data Representation using the Weyl Transform—0
Auto-clustering Output Layer: Automatic Learning of Latent Annotations in Neural Networks—0
Improvement of image classification by multiple optical scattering—0
AutoCl : A Visual Interactive System for Automatic Deep Learning Classifier Recommendation Based on Models Performance—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