# Perception for Autonomous Systems (PAZ)

arXiv: https://arxiv.org/abs/2010.14541 · HTML: https://mlsanity.com/paper/2010.14541/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [EmoSpeaker: One-shot Fine-grained Emotion-Controlled Talking Face Generation](https://mlsanity.com/paper/2402.01422/) (2024)
- [DExT: Detector Explanation Toolkit](https://mlsanity.com/paper/2212.11409/) (2022)
- [Sanity Checks for Saliency Methods Explaining Object Detectors](https://mlsanity.com/paper/2306.02424/) (2023)
- [Teaching Drones on the Fly: Can Emotional Feedback Serve as Learning Signal for Training Artificial Agents?](https://mlsanity.com/paper/2202.09634/) (2022)
- [The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learning](https://mlsanity.com/paper/2304.09914/) (2023)

## Abstract (arXiv)

In this paper we introduce the Perception for Autonomous Systems (PAZ) software library. PAZ is a hierarchical perception library that allow users to manipulate multiple levels of abstraction in accordance to their requirements or skill level. More specifically, PAZ is divided into three hierarchical levels which we refer to as pipelines, processors, and backends. These abstractions allows users to compose functions in a hierarchical modular scheme that can be applied for preprocessing, data-augmentation, prediction and postprocessing of inputs and outputs of machine learning (ML) models. PAZ uses these abstractions to build reusable training and prediction pipelines for multiple robot perception tasks such as: 2D keypoint estimation, 2D object detection, 3D keypoint discovery, 6D pose estimation, emotion classification, face recognition, instance segmentation, and attention mechanisms.