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 4501–4525 of 10420 papers

TitleStatusHype
On Evaluating the Adversarial Robustness of Semantic Segmentation Models—0
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy ImitationCode0
Meta-Gating Framework for Fast and Continuous Resource Optimization in Dynamic Wireless Environments—0
Towards quantum enhanced adversarial robustness in machine learning—0
Data-Free Backbone Fine-Tuning for Pruned Neural NetworksCode0
To Spike or Not to Spike? A Quantitative Comparison of SNN and CNN FPGA Implementations—0
A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking—0
Efficient Deep Spiking Multi-Layer Perceptrons with Multiplication-Free InferenceCode0
Annotating Ambiguous Images: General Annotation Strategy for High-Quality Data with Real-World Biomedical ValidationCode0
Benchmark data to study the influence of pre-training on explanation performance in MR image classification—0
Balanced Mixture of SuperNets for Learning the CNN Pooling ArchitectureCode0
Comparative Evaluation of Recent Universal Adversarial Perturbations in Image Classification—0
No Wrong Turns: The Simple Geometry Of Neural Networks Optimization PathsCode0
Shape Guided Gradient Voting for Domain Generalization—0
RaViTT: Random Vision Transformer Tokens—0
Pre-Pruning and Gradient-Dropping Improve Differentially Private Image Classification—0
Label-noise-tolerant medical image classification via self-attention and self-supervised learning—0
Continual Adaptation of Vision Transformers for Federated LearningCode0
Scaling Open-Vocabulary Object DetectionCode0
Enlarged Large Margin Loss for Imbalanced ClassificationCode0
Modularity Trumps Invariance for Compositional RobustnessCode0
A Comparison of Self-Supervised Pretraining Approaches for Predicting Disease Risk from Chest Radiograph Images—0
High-performance deep spiking neural networks with 0.3 spikes per neuron—0
Noise Stability Optimization for Finding Flat Minima: A Hessian-based Regularization ApproachCode0
Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP TrainingCode0
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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