OG-SGG: Ontology-Guided Scene Graph Generation. A Case Study in Transfer Learning for Telepresence Robotics

• URL: http://arxiv.org/abs/2202.10201v1
• Date: Mon, 21 Feb 2022 13:23:15 GMT
• Title: OG-SGG: Ontology-Guided Scene Graph Generation. A Case Study in Transfer Learning for Telepresence Robotics
• Authors: Fernando Amodeo, Fernando Caballero, Natalia D\'iaz-Rodr\'iguez, Luis Merino
• Abstract summary: Scene graph generation from images is a task of great interest to applications such as robotics. We propose an initial approximation to a framework called Ontology-Guided Scene Graph Generation (OG-SGG)
• Score: 124.08684545010664
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Scene graph generation from images is a task of great interest to applications such as robotics, because graphs are the main way to represent knowledge about the world and regulate human-robot interactions in tasks such as Visual Question Answering (VQA). Unfortunately, its corresponding area of machine learning is still relatively in its infancy, and the solutions currently offered do not specialize well in concrete usage scenarios. Specifically, they do not take existing "expert" knowledge about the domain world into account; and that might indeed be necessary in order to provide the level of reliability demanded by the use case scenarios. In this paper, we propose an initial approximation to a framework called Ontology-Guided Scene Graph Generation (OG-SGG), that can improve the performance of an existing machine learning based scene graph generator using prior knowledge supplied in the form of an ontology; and we present results evaluated on a specific scenario founded in telepresence robotics.

Related papers

• Learning Where to Look: Self-supervised Viewpoint Selection for Active Localization using Geometrical Information [68.10033984296247]
  This paper explores the domain of active localization, emphasizing the importance of viewpoint selection to enhance localization accuracy. Our contributions involve using a data-driven approach with a simple architecture designed for real-time operation, a self-supervised data training method, and the capability to consistently integrate our map into a planning framework tailored for real-world robotics applications.
  arXiv  Detail & Related papers  (2024-07-22T12:32:09Z)
• Graph learning in robotics: a survey [2.5726566614123874]
  The paper covers the fundamentals of graph-based models, including their architecture, training procedures, and applications. It also discusses recent advancements and challenges that arise in applied settings. The paper provides an extensive review of various robotic applications that benefit from learning on graph structures.
  arXiv  Detail & Related papers  (2023-10-06T14:52:25Z)
• SG-Bot: Object Rearrangement via Coarse-to-Fine Robotic Imagination on Scene Graphs [81.15889805560333]
  We present SG-Bot, a novel rearrangement framework. SG-Bot exemplifies lightweight, real-time, and user-controllable characteristics. Experimental results demonstrate that SG-Bot outperforms competitors by a large margin.
  arXiv  Detail & Related papers  (2023-09-21T15:54:33Z)
• A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective [71.03621840455754]
  Graph Neural Networks (GNNs) have gained momentum in graph representation learning. graph Transformers embed a graph structure into the Transformer architecture to overcome the limitations of local neighborhood aggregation. This paper presents a comprehensive review of GNNs and graph Transformers in computer vision from a task-oriented perspective.
  arXiv  Detail & Related papers  (2022-09-27T08:10:14Z)
• Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation? [54.442692221567796]
  Task specification is critical for engagement of non-expert end-users and adoption of personalized robots. A widely studied approach to task specification is through goals, using either compact state vectors or goal images from the same robot scene. In this work, we explore alternate and more general forms of goal specification that are expected to be easier for humans to specify and use.
  arXiv  Detail & Related papers  (2022-04-23T19:39:49Z)
• Situational Graphs for Robot Navigation in Structured Indoor Environments [9.13466172688693]
  We present a real-time online built Situational Graphs (S-Graphs) composed of a single graph representing the environment. Our method utilizes odometry readings and planar surfaces extracted from 3D LiDAR scans, to construct and optimize in real-time a three layered S-Graph. Our proposal does not only demonstrate state-of-the-art results for pose estimation of the robot, but also contributes with a metric-semantic-topological model of the environment.
  arXiv  Detail & Related papers  (2022-02-24T16:59:06Z)
• Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions [58.220137936626315]
  This paper extensively discusses automated graph machine learning approaches. We introduce AutoGL, our dedicated and the world's first open-source library for automated graph machine learning. Also, we describe a tailored benchmark that supports unified, reproducible, and efficient evaluations.
  arXiv  Detail & Related papers  (2022-01-04T18:31:31Z)
• An energy-based model for neuro-symbolic reasoning on knowledge graphs [0.0]
  We propose an energy-based graph embedding algorithm to characterize industrial automation systems. By combining knowledge from multiple domains, the learned model is capable of making context-aware predictions. The presented model is mappable to a biologically-inspired neural architecture, serving as a first bridge between graph embedding methods and neuromorphic computing.
  arXiv  Detail & Related papers  (2021-10-04T18:02:36Z)
• Graph Neural Networks: Methods, Applications, and Opportunities [1.2183405753834562]
  This article provides a comprehensive survey of graph neural networks (GNNs) in each learning setting. The approaches for each learning task are analyzed from both theoretical as well as empirical standpoints. Various applications and benchmark datasets are also provided, along with open challenges still plaguing the general applicability of GNNs.
  arXiv  Detail & Related papers  (2021-08-24T13:46:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.