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 8801–8825 of 10420 papers

TitleStatusHype
Efficient and Reliable Overlay Networks for Decentralized Federated Learning—0
Efficient Annotation for Medical Image Analysis: A One-Pass Selective Annotation Approach—0
Efficient CNNs via Passive Filter Pruning—0
Efficient Computation of Quantized Neural Networks by −1, +1 Encoding Decomposition—0
Efficient Converted Spiking Neural Network for 3D and 2D Classification—0
Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision—0
Efficient Deep Representation Learning by Adaptive Latent Space Sampling—0
Efficient detection of adversarial images—0
Efficient Document Image Classification Using Region-Based Graph Neural Network—0
Efficient feature embedding of 3D brain MRI images for content-based image retrieval with deep metric learning—0
TransMed: Large Language Models Enhance Vision Transformer for Biomedical Image Classification—0
Efficient Few-Shot Medical Image Analysis via Hierarchical Contrastive Vision-Language Learning—0
Efficient Fine-Tuning with Domain Adaptation for Privacy-Preserving Vision Transformer—0
Efficient Fully Distributed Federated Learning with Adaptive Local Links—0
Efficient Hyperparameter Importance Assessment for CNNs—0
Efficient Key-Based Adversarial Defense for ImageNet by Using Pre-trained Model—0
Efficient Large Scale Video Classification—0
Efficient Lung Cancer Image Classification and Segmentation Algorithm Based on Improved Swin Transformer—0
Efficiently utilizing complex-valued PolSAR image data via a multi-task deep learning framework—0
Efficient Masked Autoencoders with Self-Consistency—0
Efficient Maximal Coding Rate Reduction by Variational Forms—0
Efficient Model-Agnostic Multi-Group Equivariant Networks—0
Efficient Model Performance Estimation via Feature Histories—0
Efficient Multilingual Multi-modal Pre-training through Triple Contrastive Loss—0
Efficient Multi-Model Fusion with Adversarial Complementary Representation Learning—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
10RevCol-HTop 1 Accuracy90—Unverified