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 9501–9525 of 10420 papers

TitleStatusHype
Predicting Natural Hazards with Neuronal Networks—0
Discriminative Label Consistent Domain Adaptation—0
Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image SegmentationCode0
Unsupervised Band Selection of Hyperspectral Images via Multi-dictionary Sparse Representation—0
Weighted Linear Discriminant Analysis based on Class Saliency Information—0
Structured Label Inference for Visual UnderstandingCode0
Towards Principled Design of Deep Convolutional Networks: Introducing SimpNetCode0
CapsuleGAN: Generative Adversarial Capsule NetworkCode0
ASP:A Fast Adversarial Attack Example Generation Framework based on Adversarial Saliency Prediction—0
Deep Predictive Coding Network for Object Recognition—0
DCFNet: Deep Neural Network with Decomposed Convolutional FiltersCode0
Generative Adversarial Networks and Probabilistic Graph Models for Hyperspectral Image Classification—0
Combinets: Creativity via Recombination of Neural Networks—0
Deep Visual Domain Adaptation: A Survey—0
Batch Kalman Normalization: Towards Training Deep Neural Networks with Micro-Batches—0
Generating Triples with Adversarial Networks for Scene Graph Construction—0
Regularized Evolution for Image Classifier Architecture SearchCode0
A Method for Restoring the Training Set Distribution in an Image Classifier—0
Deep Learning Framework for Multi-class Breast Cancer Histology Image Classification—0
Deep Convolutional Neural Networks for Breast Cancer Histology Image AnalysisCode0
Causal Learning and Explanation of Deep Neural Networks via Autoencoded Activations—0
Alternating Multi-bit Quantization for Recurrent Neural Networks—0
Training Neural Networks by Using Power Linear Units (PoLUs)Code0
Cross-domain CNN for Hyperspectral Image Classification—0
Recovering from Random Pruning: On the Plasticity of Deep Convolutional Neural NetworksCode0
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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