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

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Showing 7851–7900 of 10420 papers

TitleStatusHype
FireCaffe: near-linear acceleration of deep neural network training on compute clusters—0
Recurrent Neural Networks to Correct Satellite Image Classification Maps—0
A Contrastive Symmetric Forward-Forward Algorithm (SFFA) for Continual Learning Tasks—0
A Simplified 2D-3D CNN Architecture for Hyperspectral Image Classification Based on Spatial–Spectral Fusion—0
Remote Sensing Image Classification Using Convolutional Neural Network (CNN) and Transfer Learning Techniques—0
Replay Consolidation with Label Propagation for Continual Object Detection—0
Committees of deep feedforward networks trained with few data—0
Redefining Machine Unlearning: A Conformal Prediction-Motivated Approach—0
Redesigning the classification layer by randomizing the class representation vectors—0
ReDistill: Residual Encoded Distillation for Peak Memory Reduction—0
RedSync : Reducing Synchronization Traffic for Distributed Deep Learning—0
Reduced Jeffries-Matusita distance: A Novel Loss Function to Improve Generalization Performance of Deep Classification Models—0
Confidence Adaptive Regularization for Deep Learning with Noisy Labels—0
Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients—0
A Simple Task-aware Contrastive Local Descriptor Selection Strategy for Few-shot Learning between inter class and intra class—0
Reducing Implicit Bias in Latent Domain Learning—0
Fashion and Apparel Classification using Convolutional Neural Networks—0
Reducing Textural Bias Improves Robustness of Deep Segmentation Models—0
Fashion CUT: Unsupervised domain adaptation for visual pattern classification in clothes using synthetic data and pseudo-labels—0
FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning—0
Top-Tuning: a study on transfer learning for an efficient alternative to fine tuning for image classification with fast kernel methods—0
Fine-Tuning DARTS for Image Classification—0
Fine-tuning Convolutional Neural Networks for fine art classification—0
A Framework for Fast Scalable BNN Inference using Googlenet and Transfer Learning—0
Fine-to-coarse Knowledge Transfer For Low-Res Image Classification—0
Reflecting After Learning for Understanding—0
Reflexive Guidance: Improving OoDD in Vision-Language Models via Self-Guided Image-Adaptive Concept Generation—0
Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation—0
Unified Framework for Histopathology Image Augmentation and Classification via Generative Models—0
10,000+ Times Accelerated Robust Subset Selection (ARSS)—0
Reliable Explainability of Deep Learning Spatial-Spectral Classifiers for Improved Semantic Segmentation in Autonomous Driving—0
Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis—0
Conditional Variational Autoencoder with Balanced Pre-training for Generative Adversarial Networks—0
A Framework for Double-Blind Federated Adaptation of Foundation Models—0
Relevance Prediction from Eye-movements Using Semi-interpretable Convolutional Neural Networks—0
Fast and Accurate Inference with Adaptive Ensemble Prediction for Deep Networks—0
Fine-Grained Recognition as HSnet Search for Informative Image Parts—0
Conditional Synthetic Food Image Generation—0
Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures—0
Relearning Forgotten Knowledge: on Forgetting, Overfit and Training-Free Ensembles of DNNs—0
Relevant-features based Auxiliary Cells for Energy Efficient Detection of Natural Errors—0
Reliable Probability Intervals For Classification Using Inductive Venn Predictors Based on Distance Learning—0
Fine-Grained Neural Architecture Search—0
Conditional Networks—0
Conditionally Deep Hybrid Neural Networks Across Edge and Cloud—0
Fast, Better Training Trick --- Random Gradient—0
Fine-Grained Image Classification via Combining Vision and Language—0
Fine-grained Image Classification by Exploring Bipartite-Graph Labels—0
Reinforced Attention for Few-Shot Learning and Beyond—0
Conditional Consistency Regularization for Semi-Supervised Multi-label Image Classification—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
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified