Arman Forouzesh
Interested Fields
AI, Image Processing
Title of Thesis
Transformer networks in Image Processing of Autonomous Vehicles
Description of Thesis
Deep learning (DL) approaches have been used successfully in computer vision (CV) applications. However, DL-based CV models are generally considered to be black boxes due to their lack of interpretability. This black box behavior has exacerbated user distrust and therefore has prevented widespread deployment DLCV models in autonomous driving tasks even though some of these models exhibit superiority over human performance. For this reason, it is essential to develop explainable DL models for autonomous driving tasks. Explainable DL models are able to not only boost user trust in autonomy but also serve as a diagnostic approach to identify the defects and weaknesses of the model during the system development phase. In a variety of visual benchmarks, transformer-based models perform similar to or better than other types of networks such as convolutional and recurrent neural networks. Given its high performance and less need for vision-specific inductive bias, the transformer is receiving more and more attention from the computer vision community.
Contact Information
Skype: live:.cid.249a84b242ea24ec
a.forouzesh@email.kntu.ac.ir