Scene graph generation: A comprehensive survey
Deep learning techniques have led to remarkable breakthroughs in the field of generic
object detection and have spawned a lot of scene-understanding tasks in recent years …
object detection and have spawned a lot of scene-understanding tasks in recent years …
Logic tensor networks
S Badreddine, AA Garcez, L Serafini, M Spranger - Artificial Intelligence, 2022 - Elsevier
Attempts at combining logic and neural networks into neurosymbolic approaches have been
on the increase in recent years. In a neurosymbolic system, symbolic knowledge assists …
on the increase in recent years. In a neurosymbolic system, symbolic knowledge assists …
[HTML][HTML] Scene graph generation: A comprehensive survey
Deep learning techniques have led to remarkable breakthroughs in the field of object
detection and have spawned a lot of scene-understanding tasks in recent years. Scene …
detection and have spawned a lot of scene-understanding tasks in recent years. Scene …
EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage …
N Díaz-Rodríguez, A Lamas, J Sanchez, G Franchi… - Information …, 2022 - Elsevier
Abstract The latest Deep Learning (DL) models for detection and classification have
achieved an unprecedented performance over classical machine learning algorithms …
achieved an unprecedented performance over classical machine learning algorithms …
Knowledge graph embeddings and explainable AI
F Bianchi, G Rossiello, L Costabello… - Knowledge Graphs …, 2020 - ebooks.iospress.nl
Abstract Knowledge graph embeddings are now a widely adopted approach to knowledge
representation in which entities and relationships are embedded in vector spaces. In this …
representation in which entities and relationships are embedded in vector spaces. In this …
Injecting background knowledge into embedding models for predictive tasks on knowledge graphs
Embedding models have been successfully exploited for Knowledge Graph refinement. In
these models, the data graph is projected into a low-dimensional space, in which graph …
these models, the data graph is projected into a low-dimensional space, in which graph …
Relational graph convolutional networks: a closer look
T Thanapalasingam, L van Berkel, P Bloem… - PeerJ Computer …, 2022 - peerj.com
In this article, we describe a reproduction of the Relational Graph Convolutional Network
(RGCN). Using our reproduction, we explain the intuition behind the model. Our …
(RGCN). Using our reproduction, we explain the intuition behind the model. Our …
[PDF][PDF] Learning Where and When to Reason in Neuro-Symbolic Inference.
C Cornelio, J Stuehmer, SX Hu, TM Hospedales - NeSy, 2023 - cs.ox.ac.uk
The imposition of hard constraints on the output of neural networks is a highly desirable
capability, as it instills confidence in AI by ensuring that neural network predictions adhere to …
capability, as it instills confidence in AI by ensuring that neural network predictions adhere to …
Analyzing differentiable fuzzy implications
E Van Krieken, E Acar, F Van Harmelen - arxiv preprint arxiv:2006.03472, 2020 - arxiv.org
Combining symbolic and neural approaches has gained considerable attention in the AI
community, as it is often argued that the strengths and weaknesses of these approaches are …
community, as it is often argued that the strengths and weaknesses of these approaches are …
NeuSyRE: Neuro-symbolic visual understanding and reasoning framework based on scene graph enrichment
MJ Khan, J G. Breslin, E Curry - Semantic Web, 2024 - journals.sagepub.com
Exploring the potential of neuro-symbolic hybrid approaches offers promising avenues for
seamless high-level understanding and reasoning about visual scenes. Scene Graph …
seamless high-level understanding and reasoning about visual scenes. Scene Graph …