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

Showing 18511900 of 10419 papers

TitleStatusHype
ResNet50_on_Cifar_100_Without_Transfer_LearningCode1
Efficient Deep Learning of Non-local Features for Hyperspectral Image ClassificationCode1
Distilling Visual Priors from Self-Supervised LearningCode1
Weight Excitation: Built-in Attention Mechanisms in Convolutional Neural NetworksCode1
Deep Transferring QuantizationCode1
Fixing Localization Errors to Improve Image ClassificationCode1
Mitigating Embedding and Class Assignment Mismatch in Unsupervised Image ClassificationCode1
Rethinking Recurrent Neural Networks and Other Improvements for Image ClassificationCode1
Dynamic Defense Against Byzantine Poisoning Attacks in Federated LearningCode1
Generative Classifiers as a Basis for Trustworthy Image ClassificationCode1
A General Framework For Detecting Anomalous Inputs to DNN ClassifiersCode1
The MAMe Dataset: On the relevance of High Resolution and Variable Shape image propertiesCode1
Split Computing for Complex Object Detectors: Challenges and Preliminary ResultsCode1
MaxDropout: Deep Neural Network Regularization Based on Maximum Output ValuesCode1
HATNet: An End-to-End Holistic Attention Network for Diagnosis of Breast Biopsy ImagesCode1
Deep Network Ensemble Learning applied to Image Classification using CNN TreesCode1
DEAL: Deep Evidential Active Learning for Image ClassificationCode1
CyCNN: A Rotation Invariant CNN using Polar Mapping and Cylindrical Convolution LayersCode1
NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture SearchCode1
Generative Hierarchical Features from Synthesizing ImagesCode1
PDO-eConvs: Partial Differential Operator Based Equivariant ConvolutionsCode1
Deep Learning Based Brain Tumor Segmentation: A SurveyCode1
OnlineAugment: Online Data Augmentation with Less Domain KnowledgeCode1
On Robustness and Transferability of Convolutional Neural NetworksCode1
OccamNet: A Fast Neural Model for Symbolic Regression at ScaleCode1
Unsupervised machine learning via transfer learning and k-means clustering to classify materials image dataCode1
Data-Efficient Deep Learning Method for Image Classification Using Data Augmentation, Focal Cosine Loss, and EnsembleCode1
AQD: Towards Accurate Fully-Quantized Object DetectionCode1
Patch-wise Attack for Fooling Deep Neural NetworkCode1
Concept Learners for Few-Shot LearningCode1
Learning to Learn Parameterized Classification Networks for Scalable Input ImagesCode1
Temporal Self-Ensembling Teacher for Semi-Supervised Object DetectionCode1
Adversarially-Trained Deep Nets Transfer Better: Illustration on Image ClassificationCode1
Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural NetworksCode1
AdaScale SGD: A User-Friendly Algorithm for Distributed TrainingCode1
Attack of the Tails: Yes, You Really Can Backdoor Federated LearningCode1
Generalized Few-Shot Video Classification with Video Retrieval and Feature GenerationCode1
Dynamic Group Convolution for Accelerating Convolutional Neural NetworksCode1
Discretization-Aware Architecture SearchCode1
SpinalNet: Deep Neural Network with Gradual InputCode1
GOLD-NAS: Gradual, One-Level, DifferentiableCode1
Adaptive Risk Minimization: Learning to Adapt to Domain ShiftCode1
Self-Challenging Improves Cross-Domain GeneralizationCode1
Rethinking Channel Dimensions for Efficient Model DesignCode1
Measuring Robustness to Natural Distribution Shifts in Image ClassificationCode1
Early-Learning Regularization Prevents Memorization of Noisy LabelsCode1
Ontology-guided Semantic Composition for Zero-Shot LearningCode1
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over ModulesCode1
Theory-Inspired Path-Regularized Differential Network Architecture SearchCode1
Improving robustness against common corruptions by covariate shift adaptationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94Unverified
4DaViT-GTop 1 Accuracy90.4Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
6DaViT-HTop 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10RevCol-HTop 1 Accuracy90Unverified