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 6601–6625 of 10420 papers

TitleStatusHype
Multi-layer Representation Learning for Robust OOD Image Classification—0
Benchmarking Multi-Domain Active Learning on Image Classification—0
Analysis of Catastrophic Forgetting for Random Orthogonal Transformation Tasks in the Overparameterized Regime—0
Adaptive Selection of Deep Learning Models on Embedded Systems—0
Learning a Deep ConvNet for Multi-label Classification with Partial Labels—0
Defending Malware Classification Networks Against Adversarial Perturbations with Non-Negative Weight Restrictions—0
Learning Across Decentralized Multi-Modal Remote Sensing Archives with Federated Learning—0
Multi-level Residual Networks from Dynamical Systems View—0
Post-train Black-box Defense via Bayesian Boundary Correction—0
Multilingual Image Corpus: Annotation Protocol—0
Multilingual Image Corpus – Towards a Multimodal and Multilingual Dataset—0
Defending Against Universal Perturbations With Shared Adversarial Training—0
Multi-loss ensemble deep learning for chest X-ray classification—0
Benchmarking MedMNIST dataset on real quantum hardware—0
Learned Image resizing with efficient training (LRET) facilitates improved performance of large-scale digital histopathology image classification models—0
Learned Gradient Compression for Distributed Deep Learning—0
Defending Against Image Corruptions Through Adversarial Augmentations—0
Benchmarking Inference Performance of Deep Learning Models on Analog Devices—0
Learnable Pooling Regions for Image Classification—0
Multimodal Approaches to Fair Image Classification: An Ethical Perspective—0
Defending Adversaries Using Unsupervised Feature Clustering VAE—0
Multimodal brain tumor classification—0
Benchmarking FedAvg and FedCurv for Image Classification Tasks—0
Analysis Dictionary Learning: An Efficient and Discriminative Solution—0
Adaptive sampling for scanning pixel cameras—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
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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified