Papers with Code 아카이브

Traffic Sign Recognition

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하위 태스크: 0
평가표: 10
보존된 평가 기록: 26

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

<span style="color:grey; opacity: 0.6">( Image credit: Novel Deep Learning Model for Traffic Sign Detection Using Capsule Networks )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/AdvAttack/RoadSignAttack

공식 코드

Targeted Physical-World Attention Attack on Deep Learning Models in Road Sign Recognition

https://github.com/BraunGe/TSR-Dataset-Data-Augmentation

공식 코드

Traffic Sign Recognition Dataset and Data Augmentation

https://github.com/ISorokos/SafeML

공식 코드

Keep your Distance: Determining Sampling and Distance Thresholds in Machine Learning Monitoring

https://github.com/JingWang18/Discriminative-Feature-Alignment

공식 코드

Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment

https://github.com/Omid-Nejati/Locality-iN-Locality

공식 코드

Robust Transformer with Locality Inductive Bias and Feature Normalization

https://github.com/Shawn-Shan/trapdoor

공식 코드

Gotta Catch 'Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networks

https://github.com/Sourajit2110/DilatedSkipTotalRecall

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Total Recall: Understanding Traffic Signs using Deep Hierarchical Convolutional Neural Networks

https://github.com/TrustAI/DeepGame

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A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees

https://github.com/UMBCvision/Universal-Litmus-Patterns

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Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs

https://github.com/apostovan21/binarizedneuralnetwork

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Architecturing Binarized Neural Networks for Traffic Sign Recognition

https://github.com/cas-aisafetybasicresearchgroup/causaldiff

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CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial Defense

https://github.com/charlesjin/synthetic_data

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Towards Context-Agnostic Learning Using Synthetic Data

https://github.com/christopherbrix/vnncomp2023

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Architecturing Binarized Neural Networks for Traffic Sign Recognition

https://github.com/christopherbrix/vnncomp2023_benchmarks

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Benchmarking Local Robustness of High-Accuracy Binary Neural Networks for Enhanced Traffic Sign Recognition

https://github.com/facebookresearch/vissl

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Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision

https://github.com/fklodhi/FaDec

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RED-Attack: Resource Efficient Decision based Attack for Machine Learning

https://github.com/guoyangzhao/tsclip

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TSCLIP: Robust CLIP Fine-Tuning for Worldwide Cross-Regional Traffic Sign Recognition

https://github.com/hncszyq/ShadowAttack

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Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon

https://github.com/inspire-group/advml-traffic-sign

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Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and Logos

https://github.com/jinyugy21/adv-stickers_rhde

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Adversarial Sticker: A Stealthy Attack Method in the Physical World

https://github.com/kastel-mobilitylab/attacks-on-traffic-sign-recognition

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Evaluating Adversarial Attacks on Traffic Sign Classifiers beyond Standard Baselines

https://github.com/lanfenghuanyu/Sill-Net

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Sill-Net: Feature Augmentation with Separated Illumination Representation

https://github.com/matthewwicker/adversarialrobustnesscertificationforbnns

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Adversarial Robustness Certification for Bayesian Neural Networks

https://github.com/mmasana/OoD_Mining

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Metric Learning for Novelty and Anomaly Detection

https://github.com/nicholascg/glare_dataset

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GLARE: A Dataset for Traffic Sign Detection in Sun Glare

https://github.com/olivesgatech/CURE-TSD

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Traffic Sign Detection under Challenging Conditions: A Deeper Look Into Performance Variations and Spectral Characteristics

https://github.com/pedro-morgado/nettailor

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NetTailor: Tuning the Architecture, Not Just the Weights

https://github.com/samsja/finding-a-needle-code

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Improving traffic sign recognition by active search

https://github.com/shighghyujie/newpatch-rl

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Simultaneously Optimizing Perturbations and Positions for Black-box Adversarial Patch Attacks

https://github.com/shihe98/hcb

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Watch Out! Simple Horizontal Class Backdoor Can Trivially Evade Defense

https://github.com/skokec/detectron-traffic-signs

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Deep Learning for Large-Scale Traffic-Sign Detection and Recognition

https://github.com/ssloxford/short-lived-adversarial-perturbations

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SLAP: Improving Physical Adversarial Examples with Short-Lived Adversarial Perturbations

https://github.com/zenseact/zod

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Zenseact Open Dataset: A large-scale and diverse multimodal dataset for autonomous driving

https://github.com/2K2A/2K2A.github.io

Novel Deep Learning Model for Traffic Sign Detection Using Capsule Networks

https://github.com/AdvAttack/TAA-IoT-J

Targeted Physical-World Attention Attack on Deep Learning Models in Road Sign Recognition

https://github.com/Kunkun-Jia/TSR-SA

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