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 7551–7575 of 10420 papers

TitleStatusHype
PrivyNet: A Flexible Framework for Privacy-Preserving Deep Neural Network Training—0
PRKAN: Parameter-Reduced Kolmogorov-Arnold Networks—0
Foothill: A Quasiconvex Regularization for Edge Computing of Deep Neural Networks—0
Explainable Knowledge Distillation for On-device Chest X-Ray Classification—0
Fooling Neural Networks for Motion Forecasting via Adversarial Attacks—0
CNN-based Local Vision Transformer for COVID-19 Diagnosis—0
Constrained Low-Rank Learning Using Least Squares-Based Regularization—0
Probabilistic Label Trees for Efficient Large Scale Image Classification—0
Pseudo-Bag Mixup Augmentation for Multiple Instance Learning-Based Whole Slide Image Classification—0
Probabilistic Model-Based Dynamic Architecture Search—0
Probabilistic Spatial Analysis in Quantitative Microscopy with Uncertainty-Aware Cell Detection using Deep Bayesian Regression of Density Maps—0
Explainers in the Wild: Making Surrogate Explainers Robust to Distortions through Perception—0
Fooling a Real Car with Adversarial Traffic Signs—0
A Survey and Evaluation of Adversarial Attacks for Object Detection—0
Pruning the Unlabeled Data to Improve Semi-Supervised Learning—0
Explaining Black-box Model Predictions via Two-level Nested Feature Attributions with Consistency Property—0
Food Image Classification and Segmentation with Attention-based Multiple Instance Learning—0
Pruning Ternary Quantization—0
Probing the Efficacy of Federated Parameter-Efficient Fine-Tuning of Vision Transformers for Medical Image Classification—0
Problem-dependent attention and effort in neural networks with applications to image resolution and model selection—0
Pseudo Knowledge Distillation: Towards Learning Optimal Instance-specific Label Smoothing Regularization—0
PSLT: A Light-weight Vision Transformer with Ladder Self-Attention and Progressive Shift—0
Explaining Convolutional Neural Networks by Tagging Filters—0
ProD: Prompting-To-Disentangle Domain Knowledge for Cross-Domain Few-Shot Image Classification—0
PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining—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