Rafia Rahim

I am working as a PhD student under the supervision of Prof. Dr. Andreas Zell at the chair of Cognitive System at the Eberhard Karls University of Tübingen. Currently, I am working on DeepStereoVision (a project funded by the German Ministry of Education and Research (BMBF)). The goal of this project is to build efficient deep learning algorithms for stereo vision. Specifically our main goal is to upgrade the legacy stereo vision algorithms (currently used by industrial partner Nerian) with more performant deep learning ones.

• Computer Vision
• Deep Learning

• DeepStereoVision

In this project, my main focus is to optimize deep neural networks based stereo methods to make them practically deployable in low-end devices. Precisely speaking although current state of the art deep learning based 3D stereo networks give superior performance compared to 2D stereo networks and conventional stereo methods they are not usable in production. So our goal here the goal is to optimize these method for practical deployments without sacrificing their performance.

To this end we have made following progress:

In our recent work, we empirically showed that 3D convolutions act as a major bottleneck in stereo networks. We proposed a set of “plug-&-run” separable convolutions to reduce the computational load (parameters and operations). When evaluated, these convolutions showed reduction in computational load without compromising their performance.

In another work, named MobileStereoNet we have introduced a novel volume construction method to make up for the performance compromise introduced by use of these separable convolutions. We have extended our implementation to both 2D and 3D convolutions based stereo networks. Experiments show that the proposed 2D/3D networks effectively reduce the computational cost without compromising the performance.

• Tutorial: Artificial Intelligence (Winter 2021/2022)
• Programming Project: Supervised a Software Engeneering Practical Course "Developing an Interface for Evaluating DNN-based Depth Perception Models" (Summer 2021)
• Tutorial: Artificial Intelligence (Winter 2020/2021)
• Tutorial: Introduction to Neural Networks (Summer 2020)
• Seminar: Current Topics in Deep Neural Networks (Winter 2019/20) 

We are offering following topics for Bachelor/Master Theses:

• Bachelor: Implementation of Quantization Algorithms for Model Compression
• Bachelor: Knowledge Distillation for the training of Lean Student Stereo Network
• Master: Self Supervised Learning for Deep Stereo Vision

You can find more information at Bachelor Theses and Master Theses pages. If you want to discuss the offered topics or topics of your interest drop me an email at rafia.rahimspam prevention@uni-tuebingen.de