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 3026–3050 of 10420 papers

TitleStatusHype
Hyperbolic Geometry in Computer Vision: A Survey—0
Picking Up Quantization Steps for Compressed Image ClassificationCode0
Graph based Label Enhancement for Multi-instance Multi-label learning—0
Learning Self-Supervised Representations for Label Efficient Cross-Domain Knowledge Transfer on Diabetic Retinopathy Fundus ImagesCode0
Multi-domain learning CNN model for microscopy image classification—0
Learning Bottleneck Concepts in Image ClassificationCode1
Backpropagation-free Training of Deep Physical Neural Networks—0
Get Rid Of Your Trail: Remotely Erasing Backdoors in Federated Learning—0
A baseline on continual learning methods for video action recognition—0
Multi-view Vision-Prompt Fusion Network: Can 2D Pre-trained Model Boost 3D Point Cloud Data-scarce Learning?—0
Angle based dynamic learning rate for gradient descentCode0
DCN-T: Dual Context Network with Transformer for Hyperspectral Image ClassificationCode1
Baybayin Character Instance Detection—0
ContraCluster: Learning to Classify without Labels by Contrastive Self-Supervision and Prototype-Based Semi-Supervision—0
Quantum machine learning for image classification—0
Hyperbolic Image-Text RepresentationsCode1
Performance of GAN-based augmentation for deep learning COVID-19 image classificationCode0
Do humans and machines have the same eyes? Human-machine perceptual differences on image classification—0
Visual Instruction TuningCode6
OOD-CV-v2: An extended Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images—0
Self-Supervised Learning from Non-Object Centric Images with a Geometric Transformation Sensitive ArchitectureCode0
Promises and Pitfalls of the Linearized Laplace in Bayesian OptimizationCode0
A Survey on Few-Shot Class-Incremental Learning—0
Chain of Thought Prompt Tuning in Vision Language Models—0
Autoencoders with Intrinsic Dimension Constraints for Learning Low Dimensional Image Representations—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