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 8901–8925 of 10420 papers

TitleStatusHype
Enhanced Prototypical Part Network (EPPNet) For Explainable Image Classification Via Prototypes—0
Enhancing Accuracy and Robustness through Adversarial Training in Class Incremental Continual Learning—0
Dual-Activated Lightweight Attention ResNet50 for Automatic Histopathology Breast Cancer Image Classification—0
Enhancing CNN Classification with Lamarckian Memetic Algorithms and Local Search—0
Enhancing Continuous Domain Adaptation with Multi-Path Transfer Curriculum—0
Enhancing Counterfactual Image Generation Using Mahalanobis Distance with Distribution Preferences in Feature Space—0
Enhancing Fine-grained Image Classification through Attentive Batch Training—0
Enhancing Fine-Grained Image Classifications via Cascaded Vision Language Models—0
Enhancing Generalization of First-Order Meta-Learning—0
Enhancing Glass Defect Detection with Diffusion Models: Addressing Imbalanced Datasets in Manufacturing Quality Control—0
Enhancing Image Classification in Small and Unbalanced Datasets through Synthetic Data Augmentation—0
Enhancing Instance-Level Image Classification with Set-Level Labels—0
Enhancing Interpretable Image Classification Through LLM Agents and Conditional Concept Bottleneck Models—0
Enhancing Intrinsic Features for Debiasing via Investigating Class-Discerning Common Attributes in Bias-Contrastive Pair—0
Enhancing JEPAs with Spatial Conditioning: Robust and Efficient Representation Learning—0
Enhancing Layer Attention Efficiency through Pruning Redundant Retrievals—0
Enhancing Multimodal In-Context Learning for Image Classification through Coreset Optimization—0
Enhancing Neural Training via a Correlated Dynamics Model—0
Enhancing One-run Privacy Auditing with Quantile Regression-Based Membership Inference—0
Enhancing Performance of Vision Transformers on Small Datasets through Local Inductive Bias Incorporation—0
Enhancing Post-Hoc Explanation Benchmark Reliability for Image Classification—0
Enhancing Representations through Heterogeneous Self-Supervised Learning—0
Enhancing ResNet Image Classification Performance by using Parameterized Hypercomplex Multiplication—0
Enhancing Robustness of Machine Learning Systems via Data Transformations—0
Enhancing Small Object Encoding in Deep Neural Networks: Introducing Fast&Focused-Net with Volume-wise Dot Product Layer—0
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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
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
6DaViT-HTop 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
10RevCol-HTop 1 Accuracy90—Unverified