Designing High-Performing Networks for Multi-Scale Computer Vision
Cédric Picron
Since the emergence of deep learning, the computer vision field has
flourished with models improving at a rapid pace on more and more complex
tasks. We distinguish three main ways to improve a computer vision model: (1)
improving the data aspect by for example training on a large, more diverse
dataset, (2) improving the training aspect by for example designing a better
optimizer, and (3) improving the network architecture (or network for short).
In this thesis, we chose to improve the latter, i.e. improving the network
designs of computer vision models. More specifically, we investigate new
network designs for multi-scale computer vision tasks, which are tasks
requiring to make predictions about concepts at different scales. The goal of
these new network designs is to outperform existing baseline designs from the
literature. Specific care is taken to make sure the comparisons are fair, by
guaranteeing that the different network designs were trained and evaluated with
the same settings. Code is publicly available at
https://github.com/CedricPicron/DetSeg.