Faster-LTN: a neuro-symbolic, end-to-end object detection architecture

Autor:	Manigrasso, Francesco, Miro, Filomeno Davide, Morra, Lia, Lamberti, Fabrizio
Rok vydání:	2021
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence Computer Science - Logic in Computer Science
Druh dokumentu:	Working Paper
DOI:	10.1007/978-3-030-86340-1_4
Popis:	The detection of semantic relationships between objects represented in an image is one of the fundamental challenges in image interpretation. Neural-Symbolic techniques, such as Logic Tensor Networks (LTNs), allow the combination of semantic knowledge representation and reasoning with the ability to efficiently learn from examples typical of neural networks. We here propose Faster-LTN, an object detector composed of a convolutional backbone and an LTN. To the best of our knowledge, this is the first attempt to combine both frameworks in an end-to-end training setting. This architecture is trained by optimizing a grounded theory which combines labelled examples with prior knowledge, in the form of logical axioms. Experimental comparisons show competitive performance with respect to the traditional Faster R-CNN architecture. Comment: accepted for presentation at ICANN 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2107.01877 Zobrazit plný text záznamu View this record from Arxiv