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 5601–5650 of 10420 papers

TitleStatusHype
A New Semi-supervised Learning Benchmark for Classifying View and Diagnosing Aortic Stenosis from EchocardiogramsCode1
Towards robust vision by multi-task learning on monkey visual cortexCode1
Semi-Supervised Active Learning with Temporal Output DiscrepancyCode1
PPT Fusion: Pyramid Patch Transformerfor a Case Study in Image Fusion—0
Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labelsCode1
Rethinking and Improving Relative Position Encoding for Vision Transformer—0
Self-Supervised Learning for Fine-Grained Image ClassificationCode1
United We Learn Better: Harvesting Learning Improvements From Class Hierarchies Across TasksCode0
WaveCNet: Wavelet Integrated CNNs to Suppress Aliasing Effect for Noise-Robust Image ClassificationCode1
Content-aware Directed Propagation Network with Pixel Adaptive Kernel Attention—0
A Low-Cost Neural ODE with Depthwise Separable Convolution for Edge Domain Adaptation on FPGAs—0
Dataset Distillation with Infinitely Wide Convolutional NetworksCode0
Identify Apple Leaf Diseases Using Deep Learning Algorithm—0
COPS: Controlled Pruning Before Training Starts—0
Convergence of Deep ReLU Networks—0
Towards Efficient Tensor Decomposition-Based DNN Model Compression with Optimization Framework—0
Training Energy-Efficient Deep Spiking Neural Networks with Single-Spike Hybrid Input Encoding—0
Compensation Learning—0
AASAE: Augmentation-Augmented Stochastic AutoencodersCode1
Parametric Contrastive LearningCode1
HYPER-SNN: Towards Energy-efficient Quantized Deep Spiking Neural Networks for Hyperspectral Image Classification—0
Contextual Transformer Networks for Visual RecognitionCode1
Leveraging Auxiliary Tasks with Affinity Learning for Weakly Supervised Semantic SegmentationCode1
Temporal-wise Attention Spiking Neural Networks for Event Streams Classification—0
Boosting Video Captioning with Dynamic Loss Network—0
Go Wider Instead of DeeperCode1
An Uncertainty-Aware Deep Learning Framework for Defect Detection in Casting Products—0
Multi-Label Image Classification with Contrastive Learning—0
Reconstructing Images of Two Adjacent Objects through Scattering Medium Using Generative Adversarial Network—0
Compositional Models: Multi-Task Learning and Knowledge Transfer with Modular Networks—0
Photon-Starved Scene Inference using Single Photon CamerasCode1
Pruning Ternary Quantization—0
Bias Loss for Mobile Neural NetworksCode1
RGB Image Classification with Quantum Convolutional Ansaetze—0
MCDAL: Maximum Classifier Discrepancy for Active LearningCode0
Rethinking Hard-Parameter Sharing in Multi-Domain Learning—0
Federated Learning Versus Classical Machine Learning: A Convergence Comparison—0
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural NetworksCode1
CycleMLP: A MLP-like Architecture for Dense PredictionCode1
Precision-Weighted Federated Learning—0
Understanding Gender and Racial Disparities in Image Recognition Models—0
Parametric Scattering NetworksCode1
Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed RecognitionCode1
Protecting Semantic Segmentation Models by Using Block-wise Image Encryption with Secret Key from Unauthorized Access—0
A New Clustering-Based Technique for the Acceleration of Deep Convolutional Networks—0
Non-binary deep transfer learning for image classificationCode1
CHEF: A Cheap and Fast Pipeline for Iteratively Cleaning Label Uncertainties (Technical Report)Code0
Quantum Deep Learning: Sampling Neural Nets with a Quantum Annealer—0
Just Train Twice: Improving Group Robustness without Training Group InformationCode1
OODformer: Out-Of-Distribution Detection TransformerCode1
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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