Physics

Architecture

Image segmentation

Panopticon

Pattern recognition (psychology)

Metric (unit)

Feature (linguistics)

Computer network

Scale-space segmentation

Politics

Optics

Pyramid (geometry)

Visual arts

Philosophy

Political science

Artificial intelligence

Network architecture

Economics

Operations management

Object detection

Scale (ratio)

Computer science

Linguistics

Quantum mechanics

Computer vision

Segmentation

In this work we introduce a novel, CNN-based architecture that can be trained end-to-end to deliver seamless scene segmentation results. Our goal is to predict consistent semantic segmentation and detection results by means of a panoptic output format, going beyond the simple combination of independently trained segmentation and detection models. The proposed architecture takes advantage of a novel segmentation head that seamlessly integrates multi-scale features generated by a Feature Pyramid Network with contextual information conveyed by a light-weight DeepLab-like module. As additional contribution we review the panoptic metric and propose an alternative that overcomes its limitations when evaluating non-instance categories. Our proposed network architecture yields state-of-the-art results on three challenging street-level datasets, i.e. Cityscapes, Indian Driving Dataset and Mapillary Vistas.

Seamless Scene Segmentation