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

Showing 93269350 of 10420 papers

TitleStatusHype
ARC: Anchored Representation Clouds for High-Resolution INR ClassificationCode0
PixelCAM: Pixel Class Activation Mapping for Histology Image Classification and ROI LocalizationCode0
A Joint Approach to Local Updating and Gradient Compression for Efficient Asynchronous Federated LearningCode0
Multiple Teachers-Meticulous Student: A Domain Adaptive Meta-Knowledge Distillation Model for Medical Image ClassificationCode0
Designing Stable Neural Networks using Convex Analysis and ODEsCode0
Designing Neural Network Architectures using Reinforcement LearningCode0
Depth and Representation in Vision ModelsCode0
A Rate-Distortion Framework for Explaining Neural Network DecisionsCode0
Deployment of Image Analysis Algorithms under Prevalence ShiftsCode0
Interpretable Network Visualizations: A Human-in-the-Loop Approach for Post-hoc Explainability of CNN-based Image ClassificationCode0
DENSER: Deep Evolutionary Network Structured RepresentationCode0
Dense open-set recognition with synthetic outliers generated by Real NVPCode0
DenseNet Models for Tiny ImageNet ClassificationCode0
Adaptive Cascading Network for Continual Test-Time AdaptationCode0
Multiplication fusion of sparse and collaborative-competitive representation for image classificationCode0
Real-Time Correlation Tracking via Joint Model Compression and TransferCode0
Real-Time Damage Detection in Fiber Lifting Ropes Using Lightweight Convolutional Neural NetworksCode0
Adaptive aggregation of Monte Carlo augmented decomposed filters for efficient group-equivariant convolutional neural networkCode0
Interpret Your Decision: Logical Reasoning Regularization for Generalization in Visual ClassificationCode0
A Quantization-Friendly Separable Convolution for MobileNetsCode0
Intra-class Patch Swap for Self-DistillationCode0
Multi-relation Message Passing for Multi-label Text ClassificationCode0
CAM-Based Methods Can See through WallsCode0
Multi-Sample Dropout for Accelerated Training and Better GeneralizationCode0
A Programmable Approach to Neural Network CompressionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94Unverified
4DaViT-GTop 1 Accuracy90.4Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
6DaViT-HTop 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10RevCol-HTop 1 Accuracy90Unverified