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 8026–8050 of 10420 papers

TitleStatusHype
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
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 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