Cognitive psychology

Engineering

Philosophy

Scripting language

Operating system

Joint (building)

Telecommunications

Developmental psychology

Computer science

Visual arts

Simple (philosophy)

Joint attention

Semantics (computer science)

Autism

Architecture

Segmentation

Psychology

Inference

Epistemology

Artificial intelligence

Architectural engineering

Programming language

Human–computer interaction

Machine learning

Encoder

Frame (networking)

While most approaches to semantic reasoning have focused on improving performance, in this paper we argue that computational times are very important in order to enable real time applications such as autonomous driving. Towards this goal, we present an approach to joint classification, detection and semantic segmentation using a unified architecture where the encoder is shared amongst the three tasks. Our approach is very simple, can be trained end-to-end and performs extremely well in the challenging KITTI dataset. Our approach is also very efficient, allowing us to perform inference at more then 23 frames per second. Training scripts and trained weights to reproduce our results can be found here: https://github.com/MarvinTeichmann/MultiNet

MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving