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 7301–7350 of 10420 papers

TitleStatusHype
COVIDLite: A depth-wise separable deep neural network with white balance and CLAHE for detection of COVID-19—0
Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction DetectionCode1
Keep Your AI-es on the Road: Tackling Distracted Driver Detection with Convolutional Neural Networks and Targeted Data Augmentation—0
Adaptive feature recombination and recalibration for semantic segmentation with Fully Convolutional NetworksCode0
Frost filtered scale-invariant feature extraction and multilayer perceptron for hyperspectral image classification—0
Image classification in frequency domain with 2SReLU: a second harmonics superposition activation functionCode0
SatImNet: Structured and Harmonised Training Data for Enhanced Satellite Imagery Classification—0
Semi-Supervised Recognition under a Noisy and Fine-grained Dataset—0
Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group SoftmaxCode1
Sequential Graph Convolutional Network for Active LearningCode1
Tent: Fully Test-time Adaptation by Entropy MinimizationCode1
Neural Parameter Allocation SearchCode0
Enhancing Few-Shot Image Classification with Unlabelled Examples—0
Deep Categorization with Semi-Supervised Self-Organizing MapsCode0
A block coordinate descent optimizer for classification problems exploiting convexity—0
Constraint-Based Regularization of Neural Networks—0
Multi-Subspace Neural Network for Image Recognition—0
Unsupervised Learning of Visual Features by Contrasting Cluster AssignmentsCode2
LSD-C: Linearly Separable Deep ClustersCode1
Using Wavelets and Spectral Methods to Study Patterns in Image-Classification DatasetsCode0
Visual and Textual Deep Feature Fusion for Document Image Classification—0
COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep LearningCode1
A Study of Compositional Generalization in Neural Models—0
Robust Federated Learning: The Case of Affine Distribution Shifts—0
Improving accuracy and speeding up Document Image Classification through parallel systemsCode1
Fine-Tuning DARTS for Image Classification—0
On the training dynamics of deep networks with L_2 regularizationCode0
Multiscale Deep Equilibrium ModelsCode1
Neural Ensemble Search for Uncertainty Estimation and Dataset ShiftCode1
Depth Uncertainty in Neural NetworksCode1
AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant WeightsCode1
Optimal Complexity in Decentralized Training—0
Stream-51: Streaming Classification and Novelty Detection from VideosCode1
Topology-aware Differential Privacy for Decentralized Image Classification—0
Explicitly Modeled Attention Maps for Image Classification—0
Domain Adaptation and Image Classification via Deep Conditional Adaptation Network—0
Meta Approach to Data Augmentation Optimization—0
Split-Merge Pooling—0
DTG-Net: Differentiated Teachers Guided Self-Supervised Video Action Recognition—0
Bootstrap your own latent: A new approach to self-supervised LearningCode1
CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAsCode1
BI-MAML: Balanced Incremental Approach for Meta Learning—0
Attentive Feature Reuse for Multi Task Meta learning—0
On Second Order Behaviour in Augmented Neural ODEsCode1
Move-to-Data: A new Continual Learning approach with Deep CNNs, Application for image-class recognition—0
Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networksCode0
AlgebraNetsCode0
HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach—0
Multigrid-in-Channels Architectures for Wide Convolutional Neural Networks—0
SegNBDT: Visual Decision Rules for SegmentationCode1
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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