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 9451–9500 of 10420 papers

TitleStatusHype
ClickBAIT-v2: Training an Object Detector in Real-Time—0
Recent Developments from Attribute Profiles for Remote Sensing Image Classification—0
Efficient Image Dataset Classification Difficulty Estimation for Predicting Deep-Learning AccuracyCode0
Quantum Entanglement in Deep Learning Architectures—0
Multi-scale Processing of Noisy Images using Edge Preservation LossesCode0
Lifting Layers: Analysis and ApplicationsCode0
Learning Deep Context-Network Architectures for Image Annotation—0
Detecting Adversarial Perturbations with Saliency—0
A Quantization-Friendly Separable Convolution for MobileNetsCode0
Entrenamiento de una red neuronal para el reconocimiento de imagenes de lengua de senas capturadas con sensores de profundidad—0
What do Deep Networks Like to See?Code0
MLtuner: System Support for Automatic Machine Learning TuningCode0
A Neural Markovian Multiresolution Image Labeling Algorithm—0
D^2: Decentralized Training over Decentralized Data—0
Improving Transferability of Adversarial Examples with Input DiversityCode0
Weakly Supervised Object Localization on grocery shelves using simple FCN and Synthetic Dataset—0
Fast Neural Architecture Construction using EnvelopeNets—0
Evolving Deep Convolutional Neural Networks by Variable-length Particle Swarm Optimization for Image Classification—0
TBD: Benchmarking and Analyzing Deep Neural Network Training—0
Studying Invariances of Trained Convolutional Neural Networks—0
Neural Network QuineCode0
DeepN-JPEG: A Deep Neural Network Favorable JPEG-based Image Compression Framework—0
Deep Dictionary Learning: A PARametric NETwork Approach—0
Two-Stage Convolutional Neural Network for Breast Cancer Histology Image ClassificationCode0
Speech Recognition: Keyword Spotting Through Image Recognition—0
Rethinking Feature Distribution for Loss Functions in Image ClassificationCode0
Transfer Learning with Neural AutoML—0
A Non-Technical Survey on Deep Convolutional Neural Network Architectures—0
Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network—0
Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learning—0
2^B3^C: 2 Box 3 Crop of Facial Image for Gender Classification with Convolutional Networks—0
Linking ImageNet WordNet Synsets with Wikidata—0
GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification—0
Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial ExamplesCode0
Not All Samples Are Created Equal: Deep Learning with Importance SamplingCode0
Learning Longer-term Dependencies in RNNs with Auxiliary LossesCode0
The Alpha-Beta-Symetric Divergence and their Positive Definite Kernel—0
Memory-based Parameter Adaptation—0
Learning Discriminative Multilevel Structured Dictionaries for Supervised Image Classification—0
HSI-CNN: A Novel Convolution Neural Network for Hyperspectral ImageCode0
Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning—0
Classification of breast cancer histology images using transfer learning—0
Wide Compression: Tensor Ring Nets—0
Convolutional Neural Networks combined with Runge-Kutta MethodsCode0
A Twofold Siamese Network for Real-Time Object TrackingCode0
Deep Multi-View Spatial-Temporal Network for Taxi Demand PredictionCode0
Sensitivity and Generalization in Neural Networks: an Empirical Study—0
ViS-HuD: Using Visual Saliency to Improve Human Detection with Convolutional Neural Networks—0
Learning Image Conditioned Label Space for Multilabel Classification—0
Collaboratively Weighting Deep and Classic Representation via L2 Regularization for Image ClassificationCode0
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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