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 6076–6100 of 10420 papers

TitleStatusHype
Unsupervised Domain Adaptation for Semantic Image Segmentation: a Comprehensive Survey—0
Manas: Mining Software Repositories to Assist AutoMLCode0
A Survey of Deep Learning for Low-Shot Object Detection—0
Ablation study of self-supervised learning for image classificationCode0
Make A Long Image Short: Adaptive Token Length for Vision Transformers—0
Probabilistic Tracking with Deep Factors—0
Vision Pair Learning: An Efficient Training Framework for Image Classification—0
Inferring Prototypes for Multi-Label Few-Shot Image Classification with Word Vector Guided Attention—0
argmax centroid—0
Adversarial Teacher-Student Representation Learning for Domain GeneralizationCode0
Explanation-based Data Augmentation for Image ClassificationCode0
Shallow Network Based on Depthwise Over-Parameterized Convolution for Hyperspectral Image Classification—0
Looking Beyond Single Images for Contrastive Semantic Segmentation Learning—0
Local Signal Adaptivity: Provable Feature Learning in Neural Networks Beyond KernelsCode0
Differentiable Spike: Rethinking Gradient-Descent for Training Spiking Neural Networks—0
DFTS2: Simulating Deep Feature Transmission Over Packet Loss ChannelsCode0
Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble ModelsCode0
Memory-efficient Patch-based Inference for Tiny Deep Learning—0
Learning from Mistakes based on Class Weighting with Application to Neural Architecture Search—0
Bayesian Adaptation for Covariate Shift—0
A Mathematical Framework for Quantifying Transferability in Multi-source Transfer Learning—0
Revisiting the Transferability of Supervised Pretraining: an MLP Perspective—0
Generating and Modifying Natural Language Explanations—0
CondenseNeXt: An Ultra-Efficient Deep Neural Network for Embedded SystemsCode0
Pyramid Adversarial Training Improves ViT PerformanceCode0
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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