  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.
