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 38013825 of 10420 papers

TitleStatusHype
Image Classification Using Singular Value Decomposition and OptimizationCode0
Image Classification with Classic and Deep Learning TechniquesCode0
Dataset Condensation Driven Machine UnlearningCode0
Image classification in frequency domain with 2SReLU: a second harmonics superposition activation functionCode0
CoA: Chain-of-Action for Generative Semantic LabelsCode0
ScanMix: Learning from Severe Label Noise via Semantic Clustering and Semi-Supervised LearningCode0
Image Classification of Melanoma, Nevus and Seborrheic Keratosis by Deep Neural Network EnsembleCode0
Image Classification with CondenseNeXt for ARM-Based Computing PlatformsCode0
Data Representations' Study of Latent Image ManifoldsCode0
AutoCLIP: Auto-tuning Zero-Shot Classifiers for Vision-Language ModelsCode0
Coarse-to-Fine Object Tracking Using Deep Features and Correlation FiltersCode0
Explicitly Modeling Pre-Cortical Vision with a Neuro-Inspired Front-End Improves CNN RobustnessCode0
Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language TasksCode0
Data Pruning Can Do More: A Comprehensive Data Pruning Approach for Object Re-identificationCode0
Data Parameters: A New Family of Parameters for Learning a Differentiable CurriculumCode0
Image-Caption Encoding for Improving Zero-Shot GeneralizationCode0
ILGNet: Inception Modules with Connected Local and Global Features for Efficient Image Aesthetic Quality Classification using Domain AdaptationCode0
Exploiting Invariance in Training Deep Neural NetworksCode0
Data-Free Universal Attack by Exploiting the Intrinsic Vulnerability of Deep ModelsCode0
Aligning Explanations with Human CommunicationCode0
Image classification and retrieval with random depthwise signed convolutional neural networksCode0
Comparing supervised learning dynamics: Deep neural networks match human data efficiency but show a generalisation lagCode0
Data-Free Generative Replay for Class-Incremental Learning on Imbalanced DataCode0
Identifying Bias in Deep Neural Networks Using Image TransformsCode0
Identification of Stone Deterioration Patterns with Large Multimodal ModelsCode0
Show:102550
← PrevPage 153 of 417Next →

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