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 7401–7450 of 10420 papers

TitleStatusHype
A Study for Universal Adversarial Attacks on Texture Recognition—0
Improving Network Slimming with Nonconvex RegularizationCode0
UCP: Uniform Channel Pruning for Deep Convolutional Neural Networks Compression and Acceleration—0
Consensus Clustering With Unsupervised Representation Learning—0
Online Knowledge Distillation via Multi-branch Diversity Enhancement—0
Transformers: State-of-the-Art Natural Language ProcessingCode0
A new dataset of dog breed images and a benchmark for fine-grained classification—0
Using Unlabeled Data for Increasing Low-Shot Classification Accuracy of Relevant and Open-Set Irrelevant Images—0
High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network—0
Real-time Implementation of RMNv2 Classifier in NXP Bluebox 2.0 and NXP i.MX RT1060—0
Strategy and Benchmark for Converting Deep Q-Networks to Event-Driven Spiking Neural Networks—0
The Utility of Decorrelating Colour Spaces in Vector Quantised Variational AutoencodersCode0
Attention-Aware Noisy Label Learning for Image Classification—0
Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks—0
Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification—0
Where is the Model Looking At?--Concentrate and Explain the Network Attention—0
A Comparative Study of Deep Learning Loss Functions for Multi-Label Remote Sensing Image Classification—0
MS-RANAS: Multi-Scale Resource-Aware Neural Architecture SearchCode0
Learn like a Pathologist: Curriculum Learning by Annotator Agreement for Histopathology Image Classification—0
Trustworthy Convolutional Neural Networks: A Gradient Penalized-based Approach—0
Scalable Transfer Learning with Expert Models—0
Where Does the Robustness Come from? A Study of the Transformation-based Ensemble Defence—0
Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift—0
Improving Few-Shot Visual Classification with Unlabelled Examples—0
Predicting the Outputs of Finite Networks Trained with Noisy Gradients—0
Generating Realistic COVID19 X-rays with a Mean Teacher + Transfer Learning GAN—0
Locally orderless tensor networks for classifying two- and three-dimensional medical imagesCode0
A Unified Plug-and-Play Framework for Effective Data Denoising and Robust Abstention—0
High Definition image classification in Geoscience using Machine LearningCode0
Training CNNs in Presence of JPEG Compression: Multimedia Forensics vs Computer Vision—0
PK-GCN: Prior Knowledge Assisted Image Classification using Graph Convolution Networks—0
Secure Data Sharing With Flow ModelCode0
Compressive spectral image classification using 3D coded convolutional neural network—0
Schizophrenia-mimicking layers outperform conventional neural network layersCode0
Pruning Convolutional Filters using Batch Bridgeout—0
Dynamic Fusion based Federated Learning for COVID-19 Detection—0
Role of Orthogonality Constraints in Improving Properties of Deep Networks for Image Classification—0
Integration of Roadside Camera Images and Weather Data for Monitoring Winter Road Surface Conditions—0
Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models—0
Adaptive Meta-Learning for Identification of Rover-Terrain DynamicsCode0
The DongNiao International Birds 10000 Dataset—0
Visual-Semantic Embedding Model Informed by Structured Knowledge—0
Learning Soft Labels via Meta Learning—0
Features based Mammogram Image Classification using Weighted Feature Support Vector Machine—0
Multi-Level Graph Convolutional Network with Automatic Graph Learning for Hyperspectral Image Classification—0
Adversarial Rain Attack and Defensive Deraining for DNN Perception—0
AdderSR: Towards Energy Efficient Image Super-Resolution—0
Hyperspectral Image Classification Method Based on 2D–3D CNN and Multibranch Feature Fusion—0
Generating Efficient DNN-Ensembles with Evolutionary Computation—0
Noisy Concurrent Training for Efficient Learning under Label NoiseCode0
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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