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 7101–7125 of 10420 papers

TitleStatusHype
On the Neural Tangent Kernel Analysis of Randomly Pruned Neural Networks—0
End-to-end optimized image compression for multiple machine tasks—0
Parallel Proportional Fusion of Spiking Quantum Neural Network for Optimizing Image Classification—0
A Framework for Generalizing Critical Heat Flux Detection Models Using Unsupervised Image-to-Image Translation—0
Active Generative Adversarial Network for Image Classification—0
Fully Hyperbolic Convolutional Neural Networks—0
Fully Convolutional Neural Networks to Detect Clinical Dermoscopic Features—0
On the rate of convergence of image classifiers based on convolutional neural networks—0
Energy-efficient Amortized Inference with Cascaded Deep Classifiers—0
On the Reproducibility of Neural Network Predictions—0
A Study of Image Analysis with Tangent Distance—0
Pancreatic Cancer ROSE Image Classification Based on Multiple Instance Learning with Shuffle Instances—0
On the Robustness of Large Multimodal Models Against Image Adversarial Attacks—0
Energy-Efficient Classification at the Wireless Edge with Reliability Guarantees—0
Unsupervised Continual Learning Via Pseudo Labels—0
Fully Connected Deep Structured Networks—0
On the Sampling Strategy for Evaluation of Spectral-spatial Methods in Hyperspectral Image Classification—0
On the Shift Invariance of Max Pooling Feature Maps in Convolutional Neural Networks—0
Abnormal Client Behavior Detection in Federated Learning—0
On the Surprising Effectiveness of Transformers in Low-Labeled Video Recognition—0
Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels—0
On the Transferability of Adversarial Examples between Encrypted Models—0
On the universality of neural encodings in CNNs—0
On the unreasonable vulnerability of transformers for image restoration -- and an easy fix—0
Full-attention based Neural Architecture Search using Context Auto-regression—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
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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified