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 7851–7875 of 10420 papers

TitleStatusHype
Recurrent Multiresolution Convolutional Networks for VHR Image Classification—0
Recurrent Neural Networks to Correct Satellite Image Classification Maps—0
A Contrastive Symmetric Forward-Forward Algorithm (SFFA) for Continual Learning Tasks—0
FireCaffe: near-linear acceleration of deep neural network training on compute clusters—0
A Simplified 2D-3D CNN Architecture for Hyperspectral Image Classification Based on Spatial–Spectral Fusion—0
Reinforcing Medical Image Classifier to Improve Generalization on Small Datasets—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
Relevant-features based Auxiliary Cells for Energy Efficient Detection of Natural Errors—0
Remote Sensing Image Classification with Decoupled Knowledge Distillation—0
Confidence Adaptive Regularization for Deep Learning with Noisy Labels—0
Reducing Implicit Bias in Latent Domain Learning—0
Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients—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
A Simple Task-aware Contrastive Local Descriptor Selection Strategy for Few-shot Learning between inter class and intra class—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
Reinforcement Explanation Learning—0
Fine-tuning Convolutional Neural Networks for fine art 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
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