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 8901–8950 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
Enhancing the Convergence of Federated Learning Aggregation Strategies with Limited Data—0
Enhancing the Performance of Convolutional Neural Networks on Quality Degraded Datasets—0
Enhancing the Regularization Effect of Weight Pruning in Artificial Neural Networks—0
Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study—0
Enhancing Transformers Through Conditioned Embedded Tokens—0
Enhancing Whole Slide Image Classification through Supervised Contrastive Domain Adaptation—0
Enrich the content of the image Using Context-Aware Copy Paste—0
Ensemble learning of diffractive optical networks—0
Ensemble of CNN classifiers using Sugeno Fuzzy Integral Technique for Cervical Cytology Image Classification—0
Ensemble of Models Trained by Key-based Transformed Images for Adversarially Robust Defense Against Black-box Attacks—0
Ensemble of Part Detectors for Simultaneous Classification and Localization—0
Ensembles of feedforward-designed convolutional neural networks—0
Ensemble Soft-Margin Softmax Loss for Image Classification—0
Ensuring superior learning outcomes and data security for authorized learner—0
Entanglement and Tensor Networks for Supervised Image Classification—0
Entanglement Entropy of Target Functions for Image Classification and Convolutional Neural Network—0
Entrenamiento de una red neuronal para el reconocimiento de imagenes de lengua de senas capturadas con sensores de profundidad—0
Entropy-based Guidance of Deep Neural Networks for Accelerated Convergence and Improved Performance—0
Entropy-Driven Mixed-Precision Quantization for Deep Network Design—0
Entropy Induced Pruning Framework for Convolutional Neural Networks—0
Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models—0
Equivariance with Learned Canonicalization Functions—0
Equivariant Neural Tangent Kernels—0
EraseReLU: A Simple Way to Ease the Training of Deep Convolution Neural Networks—0
Erasing Integrated Learning: A Simple Yet Effective Approach for Weakly Supervised Object Localization—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