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 24762500 of 10420 papers

TitleStatusHype
Instance-dependent Label Distribution Estimation for Learning with Label NoiseCode0
Instance Temperature Knowledge DistillationCode0
Adversarial Examples Improve Image RecognitionCode0
Instilling Inductive Biases with SubnetworksCode0
An Intelligent Remote Sensing Image Quality Inspection SystemCode0
Interpretable and Interactive Deep Multiple Instance Learning for Dental Caries Classification in Bitewing X-raysCode0
Input Invex Neural NetworkCode0
Instance-based Label Smoothing For Better Calibrated Classification NetworksCode0
Initialization Matters for Adversarial Transfer LearningCode0
In-Place Activated BatchNorm for Memory-Optimized Training of DNNsCode0
Application of a Convolutional Neural Network for image classification to the analysis of collisions in High Energy PhysicsCode0
Information Competing Process for Learning Diversified RepresentationsCode0
Input-gradient space particle inference for neural network ensemblesCode0
Instance Enhancement Batch Normalization: an Adaptive Regulator of Batch NoiseCode0
In-domain representation learning for remote sensingCode0
AP-Perf: Incorporating Generic Performance Metrics in Differentiable LearningCode0
Inference via Sparse Coding in a Hierarchical Vision ModelCode0
Class Prototype-based Cleaner for Label Noise LearningCode0
InDL: A New Dataset and Benchmark for In-Diagram Logic Interpretation based on Visual IllusionCode0
Influence of Image Classification Accuracy on Saliency Map EstimationCode0
CLASS-M: Adaptive stain separation-based contrastive learning with pseudo-labeling for histopathological image classificationCode0
Revisiting 16-bit Neural Network Training: A Practical Approach for Resource-Limited LearningCode0
Class-Level Logit PerturbationCode0
12 mJ per Class On-Device Online Few-Shot Class-Incremental LearningCode0
In-distribution Public Data Synthesis with Diffusion Models for Differentially Private Image ClassificationCode0
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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