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 8001–8050 of 10420 papers

TitleStatusHype
Image-Based Feature Representation for Insider Threat Classification—0
Momentum Contrast for Unsupervised Visual Representation LearningCode3
Knowledge Representing: Efficient, Sparse Representation of Prior Knowledge for Knowledge Distillation—0
Cost-efficient segmentation of electron microscopy images using active learning—0
Pose Guided Attention for Multi-label Fashion Image Classification—0
Throughput Prediction of Asynchronous SGD in TensorFlow—0
Learning From Brains How to Regularize MachinesCode0
A Computing Kernel for Network Binarization on PyTorchCode0
Self-training with Noisy Student improves ImageNet classificationCode1
An empirical study of the relation between network architecture and complexity—0
Meta Label Correction for Noisy Label LearningCode0
IrisNet: Deep Learning for Automatic and Real-time Tongue Contour Tracking in Ultrasound Video Data using Peripheral Vision—0
Optimizing Deep Learning Inference on Embedded Systems Through Adaptive Model Selection—0
On the design of convolutional neural networks for automatic detection of Alzheimer's diseaseCode0
Improving Machine Reading Comprehension via Adversarial Training—0
On the Relationship between Self-Attention and Convolutional LayersCode0
Knowledge Distillation for Incremental Learning in Semantic Segmentation—0
Efficacy of Pixel-Level OOD Detection for Semantic Segmentation—0
Hyperspectral Image Classification via Sparse Representation With Incremental DictionariesCode0
A Programmable Approach to Neural Network CompressionCode0
Towards Large yet Imperceptible Adversarial Image Perturbations with Perceptual Color DistanceCode0
Coverage Guided Testing for Recurrent Neural Networks—0
A Spectral Nonlocal Block for Neural Networks—0
An Algorithm for Routing Capsules in All Domains—0
Self-Adaptive Scaling for Learnable Residual Structure—0
Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers—0
ALERT: Accurate Learning for Energy and Timeliness—0
Hierarchical Expert Networks for Meta-Learning—0
Very high resolution Airborne PolSAR Image Classification using Convolutional Neural Networks—0
Deep Metric Learning-Based Feature Embedding for Hyperspectral Image ClassificationCode0
Are Out-of-Distribution Detection Methods Effective on Large-Scale Datasets?—0
Multimodal Model-Agnostic Meta-Learning via Task-Aware ModulationCode1
Training Set Effect on Super Resolution for Automated Target Recognition—0
Decomposable-Net: Scalable Low-Rank Compression for Neural NetworksCode0
Best Practices for Convolutional Neural Networks Applied to Object Recognition in Images—0
LeanConvNets: Low-cost Yet Effective Convolutional Neural Networks—0
Neighborhood Watch: Representation Learning with Local-Margin Triplet Loss and Sampling Strategy for K-Nearest-Neighbor Image Classification—0
Shoestring: Graph-Based Semi-Supervised Learning with Severely Limited Labeled DataCode0
Secure Evaluation of Quantized Neural Networks—0
Asynchronous Decentralized SGD with Quantized and Local Updates—0
Spectral Algorithm for Low-rank Multitask Regression—0
Deep Learning for Hyperspectral Image Classification: An Overview—0
LPRNet: Lightweight Deep Network by Low-rank Pointwise Residual Convolution—0
Adversarial Feature Alignment: Avoid Catastrophic Forgetting in Incremental Task Lifelong Learning—0
Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning—0
Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks—0
Occlusions for Effective Data Augmentation in Image Classification—0
Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer OutputCode0
A deep active learning system for species identification and counting in camera trap imagesCode1
Kernel computations from large-scale random features obtained by Optical Processing UnitsCode0
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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