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 6351–6375 of 10420 papers

TitleStatusHype
Asynchronous Hierarchical Federated Learning—0
Glasses Detection Using Convolutional Neural Networks—0
MiSuRe is all you need to explain your image segmentation—0
Active Learning Under Malicious Mislabeling and Poisoning Attacks—0
Multi-label Image Classification using Adaptive Graph Convolutional Networks: from a Single Domain to Multiple Domains—0
Multi-Label Image Classification with Regional Latent Semantic Dependencies—0
Mitigating Bias: Enhancing Image Classification by Improving Model Explanations—0
Give Me Your Attention: Dot-Product Attention Considered Harmful for Adversarial Patch Robustness—0
Convolutional Spiking Neural Network for Image Classification—0
GIST: Greedy Independent Set Thresholding for Diverse Data Summarization—0
Mitochondria-based Renal Cell Carcinoma Subtyping: Learning from Deep vs. Flat Feature Representations—0
MixDefense: A Defense-in-Depth Framework for Adversarial Example Detection Based on Statistical and Semantic Analysis—0
Mixed-Block Neural Architecture Search for Medical Image Segmentation—0
Mixed-Precision Quantized Neural Network with Progressively Decreasing Bitwidth For Image Classification and Object Detection—0
Mixed-Privacy Forgetting in Deep Networks—0
Asynchronous Bioplausible Neuron for SNN for Event Vision—0
Convolutional Patch Representations for Image Retrieval: an Unsupervised Approach—0
Mixer: DNN Watermarking using Image Mixup—0
Does Visual Pretraining Help End-to-End Reasoning?—0
Does Saliency-Based Training bring Robustness for Deep Neural Networks in Image Classification?—0
GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning—0
Multi-Instance Multi-Scale CNN for Medical Image Classification—0
Gibbs Sampling with Low-Power Spiking Digital Neurons—0
MixModule: Mixed CNN Kernel Module for Medical Image Segmentation—0
A Gradient-based Kernel Approach for Efficient Network Architecture Search—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