Poisoning and evasion attacks against deep learning algorithms in autonomous vehicles
With the ongoing development and improvement of deep learning technology, autonomous
vehicles (AVs) have made tremendous progress in recent years. Despite its great potential …
vehicles (AVs) have made tremendous progress in recent years. Despite its great potential …
Pcgan: A noise robust conditional generative adversarial network for one shot learning
Traffic sign classification plays a vital role in autonomous vehicles for its powerful capability
in information representation. However, the low-quality data of traffic signs captured by in …
in information representation. However, the low-quality data of traffic signs captured by in …
An efficient small traffic sign detection method based on YOLOv3
J Wan, W Ding, H Zhu, M Xia, Z Huang, L Tian… - Journal of Signal …, 2021 - Springer
In recent years, target detection framework based on deep learning has made brilliant
achievements. However, real-life traffic sign detection remains a great challenge for most of …
achievements. However, real-life traffic sign detection remains a great challenge for most of …
Variational prototyping-encoder: One-shot learning with prototypical images
In daily life, graphic symbols, such as traffic signs and brand logos, are ubiquitously utilized
around us due to its intuitive expression beyond language boundary. We tackle an open-set …
around us due to its intuitive expression beyond language boundary. We tackle an open-set …
Sill-net: Feature augmentation with separated illumination representation
For visual object recognition tasks, the illumination variations can cause distinct changes in
object appearance and thus confuse the deep neural network based recognition models …
object appearance and thus confuse the deep neural network based recognition models …
AAPL: Adding Attributes to Prompt Learning for Vision-Language Models
Recent advances in large pre-trained vision-language models have demonstrated
remarkable performance on zero-shot downstream tasks. Building upon this recent studies …
remarkable performance on zero-shot downstream tasks. Building upon this recent studies …
Data poisoning attacks in internet-of-vehicle networks: Taxonomy, state-of-the-art, and future directions
With the unprecedented development of deep learning, autonomous vehicles (AVs) have
achieved tremendous progress nowadays. However, AV supported by DNN models is …
achieved tremendous progress nowadays. However, AV supported by DNN models is …
Disco: Influence maximization meets network embedding and deep learning
Since its introduction in 2003, the influence maximization (IM) problem has drawn significant
research attention in the literature. The aim of IM is to select a set of k users who can …
research attention in the literature. The aim of IM is to select a set of k users who can …
Road feature detection for advance driver assistance system using deep learning
Hundreds of people are injured or killed in road accidents. These accidents are caused by
several intrinsic and extrinsic factors, including the attentiveness of the driver towards the …
several intrinsic and extrinsic factors, including the attentiveness of the driver towards the …
SeqNet: Sequential networks for one-shot traffic sign recognition with transfer learning
In traffic sign recognition tasks, recognition of road signs by observing synthetic reference
images is a human-like ability that can be performed by one-shot learning algorithms. One …
images is a human-like ability that can be performed by one-shot learning algorithms. One …