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 92269250 of 10420 papers

TitleStatusHype
CBGT-Net: A Neuromimetic Architecture for Robust Classification of Streaming DataCode0
ARMA Nets: Expanding Receptive Field for Dense PredictionCode0
Aligning Explanations with Human CommunicationCode0
Arithmetic addition of two integers by deep image classification networks: experiments to quantify their autonomous reasoning abilityCode0
CBAM: Convolutional Block Attention ModuleCode0
Multi-layered tensor networks for image classificationCode0
A Lightweight Privacy-Preserving Scheme Using Label-based Pixel Block Mixing for Image Classification in Deep LearningCode0
DFM-X: Augmentation by Leveraging Prior Knowledge of Shortcut LearningCode0
Multi-Level and Multi-Scale Feature Aggregation Using Pre-trained Convolutional Neural Networks for Music Auto-taggingCode0
Multi-Level Correlation Network For Few-Shot Image ClassificationCode0
Multi-Level Feature Distillation of Joint Teachers Trained on Distinct Image DatasetsCode0
AlgebraNetsCode0
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral FiltersCode0
Revisiting 16-bit Neural Network Training: A Practical Approach for Resource-Limited LearningCode0
Rethinking Feature Distribution for Loss Functions in Image ClassificationCode0
DFE-IANet: A Method for Polyp Image Classification Based on Dual-domain Feature Extraction and Interaction AttentionCode0
In-distribution Public Data Synthesis with Diffusion Models for Differentially Private Image ClassificationCode0
InDL: A New Dataset and Benchmark for In-Diagram Logic Interpretation based on Visual IllusionCode0
In-domain representation learning for remote sensingCode0
A Large-Scale Empirical Study on Improving the Fairness of Image Classification ModelsCode0
Multi-level Second-order Few-shot LearningCode0
Adaptive Convolution Kernel for Artificial Neural NetworksCode0
Bayesian Statistics Guided Label Refurbishment Mechanism: Mitigating Label Noise in Medical Image ClassificationCode0
Multilingual Vision-Language Pre-training for the Remote Sensing DomainCode0
A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural NetworksCode0
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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
5DaViT-HTop 1 Accuracy90.2Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90Unverified