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 6201–6250 of 10420 papers

TitleStatusHype
PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy LabelsCode0
GCCN: Global Context Convolutional Network—0
FedGEMS: Federated Learning of Larger Server Models via Selective Knowledge Fusion—0
EnGraf-Net: Multiple Granularity Branch Network with Fine-Coarse Graft Grained for Classification TaskCode0
Improving the Deployment of Recycling Classification through Efficient Hyper-Parameter Analysis—0
Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model BiasCode0
Repaint: Improving the Generalization of Down-Stream Visual Tasks by Generating Multiple Instances of Training ExamplesCode0
When in Doubt, Summon the Titans: Efficient Inference with Large Models—0
Multi-concept adversarial attacks—0
An Adaptive Sampling and Edge Detection Approach for Encoding Static Images for Spiking Neural Networks—0
Improving Tail-Class Representation with Centroid Contrastive Learning—0
DARTS for Inverse Problems: a Study on Stability—0
EmbRace: Accelerating Sparse Communication for Distributed Training of NLP Neural Networks—0
Deep CNNs for Peripheral Blood Cell Classification—0
Network Augmentation for Tiny Deep Learning—0
Alleviating Noisy-label Effects in Image Classification via Probability Transition Matrix—0
Contrastive Learning of Visual-Semantic Embeddings—0
Online Continual Learning Via Candidates Voting—0
TESDA: Transform Enabled Statistical Detection of Attacks in Deep Neural NetworksCode0
Neural Network Pruning Through Constrained Reinforcement Learning—0
Pro-KD: Progressive Distillation by Following the Footsteps of the Teacher—0
FedSLD: Federated Learning with Shared Label Distribution for Medical Image Classification—0
Trade-offs of Local SGD at Scale: An Empirical Study—0
Automated Quality Control of Vacuum Insulated Glazing by Convolutional Neural Network Image Classification—0
Grouped Pointwise Convolutions Significantly Reduces Parameters in EfficientNetCode0
Adversarial Attack across Datasets—0
Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers—0
Transform and Bitstream Domain Image Classification—0
Dynamic Inference with Neural Interpreters—0
Bio-inspired learnable divisive normalization for ANNs—0
Balancing Average and Worst-case Accuracy in Multitask Learning—0
CovXR: Automated Detection of COVID-19 Pneumonia in Chest X-Rays through Machine Learning—0
Voice-assisted Image Labelling for Endoscopic Ultrasound Classification using Neural Networks—0
A Closer Look at Prototype Classifier for Few-shot Image Classification—0
Semi-Supervised Auto-Encoder Graph Network for Diabetic Retinopathy Grading—0
Learnable Adaptive Cosine Estimator (LACE) for Image ClassificationCode0
Instance-based Label Smoothing For Better Calibrated Classification NetworksCode0
Decomposing Convolutional Neural Networks into Reusable and Replaceable Modules—0
Homogeneous Learning: Self-Attention Decentralized Deep LearningCode0
Synthesizing Machine Learning Programs with PAC Guarantees via Statistical Sketching—0
DenseNet approach to segmentation and classification of dermatoscopic skin lesions imagesCode0
SGMNet: Scene Graph Matching Network for Few-Shot Remote Sensing Scene Classification—0
UniNet: Unified Architecture Search with Convolution, Transformer, and MLP—0
PAC Synthesis of Machine Learning Programs—0
Observations on K-image Expansion of Image-Mixing Augmentation for ClassificationCode0
Image Compression and Classification Using Qubits and Quantum Deep Learning—0
A Genetic Programming Approach To Zero-Shot Neural Architecture Ranking—0
ViDT: An Efficient and Effective Fully Transformer-based Object Detector—0
MSHCNet: Multi-Stream Hybridized Convolutional Networks with Mixed Statistics in Euclidean/Non-Euclidean Spaces and Its Application to Hyperspectral Image Classification—0
Using Contrastive Learning and Pseudolabels to learn representations for Retail Product 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
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