# Architecture Search of Dynamic Cells for Semantic Video Segmentation

## TL;DR

This paper introduces a neural architecture search method to automatically design dynamic cells for semantic video segmentation, achieving stable, accurate results without optical flow computation in just 2 GPU-days.

## Contribution

It proposes a novel NAS-based approach for dynamic cell architecture design in video segmentation, reducing reliance on manual design and optical flow.

## Key findings

- Achieves high accuracy on CityScapes and CamVid datasets.
- Requires only 2 GPU-days for architecture search.
- Does not depend on optical flow computation.

## Abstract

In semantic video segmentation the goal is to acquire consistent dense semantic labelling across image frames. To this end, recent approaches have been reliant on manually arranged operations applied on top of static semantic segmentation networks - with the most prominent building block being the optical flow able to provide information about scene dynamics. Related to that is the line of research concerned with speeding up static networks by approximating expensive parts of them with cheaper alternatives, while propagating information from previous frames. In this work we attempt to come up with generalisation of those methods, and instead of manually designing contextual blocks that connect per-frame outputs, we propose a neural architecture search solution, where the choice of operations together with their sequential arrangement are being predicted by a separate neural network. We showcase that such generalisation leads to stable and accurate results across common benchmarks, such as CityScapes and CamVid datasets. Importantly, the proposed methodology takes only 2 GPU-days, finds high-performing cells and does not rely on the expensive optical flow computation.