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Traffic Sign Recognition

Traffic sign recognition is the task of recognising traffic signs in an image or video.

( Image credit: Novel Deep Learning Model for Traffic Sign Detection Using Capsule Networks )

Papers

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Showing 1–50 of 127 papers

TitleStatusHype
Simultaneously Optimizing Perturbations and Positions for Black-box Adversarial Patch AttacksCode1
Keep your Distance: Determining Sampling and Distance Thresholds in Machine Learning MonitoringCode1
Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural PhenomenonCode1
SLAP: Improving Physical Adversarial Examples with Short-Lived Adversarial PerturbationsCode1
Zenseact Open Dataset: A large-scale and diverse multimodal dataset for autonomous drivingCode1
Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent AlignmentCode1
Adversarial Sticker: A Stealthy Attack Method in the Physical WorldCode1
Robust Transformer with Locality Inductive Bias and Feature NormalizationCode1
Benchmarking Local Robustness of High-Accuracy Binary Neural Networks for Enhanced Traffic Sign RecognitionCode1
CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial DefenseCode1
Toward Improving Confidence in Autonomous Vehicle Software: A Study on Traffic Sign Recognition SystemsCode1
Sill-Net: Feature Augmentation with Separated Illumination RepresentationCode1
GLARE: A Dataset for Traffic Sign Detection in Sun GlareCode1
A real-time and high-precision method for small traffic-signs recognitionCode1
DARTS: Deceiving Autonomous Cars with Toxic SignsCode0
CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign RecognitionCode0
Traffic Sign Detection under Challenging Conditions: A Deeper Look Into Performance Variations and Spectral CharacteristicsCode0
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without SupervisionCode0
Adversarial Robustness Certification for Bayesian Neural NetworksCode0
Traffic Sign Classification Using Deep Inception Based Convolutional NetworksCode0
TSCLIP: Robust CLIP Fine-Tuning for Worldwide Cross-Regional Traffic Sign RecognitionCode0
Evaluating Adversarial Attacks on Traffic Sign Classifiers beyond Standard BaselinesCode0
SeqNet: Sequential Networks for One-Shot Traffic Sign Recognition With Transfer LearningCode0
Traffic Sign Recognition Dataset and Data AugmentationCode0
Novel Deep Learning Model for Traffic Sign Detection Using Capsule NetworksCode0
Universal Litmus Patterns: Revealing Backdoor Attacks in CNNsCode0
NetTailor: Tuning the Architecture, Not Just the WeightsCode0
Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and LogosCode0
Targeted Physical-World Attention Attack on Deep Learning Models in Road Sign RecognitionCode0
Towards Context-Agnostic Learning Using Synthetic DataCode0
Architecturing Binarized Neural Networks for Traffic Sign RecognitionCode0
A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light DetectionCode0
Gotta Catch 'Em All: Using Honeypots to Catch Adversarial Attacks on Neural NetworksCode0
Watch Out! Simple Horizontal Class Backdoor Can Trivially Evade DefenseCode0
Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methodsCode0
Deep Learning for Large-Scale Traffic-Sign Detection and RecognitionCode0
A Game-Based Approximate Verification of Deep Neural Networks with Provable GuaranteesCode0
Improving traffic sign recognition by active searchCode0
MicronNet: A Highly Compact Deep Convolutional Neural Network Architecture for Real-time Embedded Traffic Sign ClassificationCode0
Multi-column Deep Neural Networks for Image ClassificationCode0
Metric Learning for Novelty and Anomaly DetectionCode0
RED-Attack: Resource Efficient Decision based Attack for Machine LearningCode0
Total Recall: Understanding Traffic Signs using Deep Hierarchical Convolutional Neural NetworksCode0
Data augmentation with Symbolic-to-Real Image Translation GANs for Traffic Sign Recognition—0
Why Autonomous Vehicles Are Not Ready Yet: A Multi-Disciplinary Review of Problems, Attempted Solutions, and Future Directions—0
Cross-domain Few-shot In-context Learning for Enhancing Traffic Sign Recognition—0
A novel pLSA based Traffic Signs Classification System—0
Adversarial Robustness Through Artifact Design—0
Co-domain Embedding using Deep Quadruplet Networks for Unseen Traffic Sign Recognition—0
Analysis of Classifier Training on Synthetic Data for Cross-Domain Datasets—0
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