Perception for Autonomous Systems (PAZ)
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Oct 2020.
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Citing papers per year: 2022: 2; 2023: 2; 2024: 1.
5 papers.
- 2024EmoSpeaker: One-shot Fine-grained Emotion-Controlled Talking Face Generationcited by 4
- 2022DExT: Detector Explanation Toolkitcited by 2
- 2023Sanity Checks for Saliency Methods Explaining Object Detectorscited by 2
- 2022Teaching Drones on the Fly: Can Emotional Feedback Serve as Learning Signal for Training Artificial Agents?cited by 0
- 2023The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learningcited by 0
- 2024EmoSpeaker: One-shot Fine-grained Emotion-Controlled Talking Face Generation2 Feb 2024
- 2023Sanity Checks for Saliency Methods Explaining Object Detectors4 Jun 2023
- 2023The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learning19 Apr 2023
- 2022DExT: Detector Explanation Toolkit21 Dec 2022
- 2022Teaching Drones on the Fly: Can Emotional Feedback Serve as Learning Signal for Training Artificial Agents?19 Feb 2022