This method exploits guidance from depth estimation in order to adapt the semantic segmentation model to the new scene. In this project, we will focus on the method proposed in a recent paper called DADA ( ).

To overcome the adaptation issue, researchers proposed several domain adaptation techniques. In addition to that, for semantic segmentation it is particularly costly to annotate all the pixels in modern HD images with the proper label. Semantic segmentation is one of the fundamental tasks of computer vision, where each pixel of the image is assigned to the proper object/category (or in other words we are searching for sets of pixels with the same properties in the image).

This is the case also for semantic segmentation models. However, a model performance dramatically drops when deployed on data from a different domain. Supervisor: Andrea Pilzer Learning based Computer Vision is a powerful tool nowadays, it can achieve high accuracy when trained on enough data. Topic #2: Depth Guided Adaptation for Semantic Segmentation Topic available also for a group: yes (max 2) Supervisor (name and email): Vladimir Kuzmanovski, Jaakko Hollmén

Basic knowledge in mathematical optimisation, machine learning and probability (bayesian paradigm).

Programming skills and experience with R(preferable) or Python.

The research project aims at comparison of various approaches in terms of their efficiency in guiding the search through high-dimensional spaces. The scope of the proposed research topic includes implementation/adoption of existing solutions for high-dimensional problems on calibrating mobility simulation model for a virtual city and real case-study - Tallinn. Thus, number of simulations is the bottleneck towards convergence in global optima, and requires selection of both well-informative surrogate model and utility function that efficiently manage the exploration-exploitation trade-off.

Calibration of such models for a city or region involves tuning from couples of hundreds to thousands parameters, along with computationally expensive simulations. As a consequence, bayesian optimisation is suitable for optimisation problems with expensive evaluation processes (in terms of resources like computation time, budget, etc), which highly correspond to agent-based simulation models on urban mobility. It comprises a surrogate model that maps the data to a utility, which guide the search through the objective space. Topic #1: Bayesian optimisation for calibrating agent-based simulation model on urban mobilityīackground: Bayesian optimisation is an iterative surrogate-based global optimisation approach that does not consider any assumptions on the functional form of a given optimisation problem, making it suitable for black-box functions.

Object fusion wiki