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 4601–4625 of 10420 papers

TitleStatusHype
Does Deep Active Learning Work in the Wild?—0
Automatic Discovery and Optimization of Parts for Image Classification—0
Deep Active Ensemble Sampling For Image Classification—0
Automatic Detection and Image Recognition of Precision Agriculture for Citrus Diseases—0
Access Control with Encrypted Feature Maps for Object Detection Models—0
AI-Augmented Thyroid Scintigraphy for Robust Classification—0
Impact of Regularization on Calibration and Robustness: from the Representation Space Perspective—0
Decoupling Feature Extraction and Classification Layers for Calibrated Neural Networks—0
Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyond—0
All-You-Can-Fit 8-Bit Flexible Floating-Point Format for Accurate and Memory-Efficient Inference of Deep Neural Networks—0
Impact of Light and Shadow on Robustness of Deep Neural Networks—0
Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning—0
Automatic Classification of Human Epithelial Type 2 Cell Indirect Immunofluorescence Images using Cell Pyramid Matching—0
All-Transfer Learning for Deep Neural Networks and its Application to Sepsis Classification—0
Decomposition-Based Transfer Distance Metric Learning for Image Classification—0
Impact of Feedback Type on Explanatory Interactive Learning—0
Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks—0
Impact of Scaled Image on Robustness of Deep Neural Networks—0
Importance of the Mathematical Foundations of Machine Learning Methods for Scientific and Engineering Applications—0
Decomposing Convolutional Neural Networks into Reusable and Replaceable Modules—0
Automatic Classification and Segmentation of Tunnel Cracks Based on Deep Learning and Visual Explanations—0
Impact of base dataset design on few-shot image classification—0
Automatic and Quantitative evaluation of attribute discovery methods—0
Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features—0
All-Photonic Artificial Neural Network Processor Via Non-linear Optics—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