#5819. Semi-lexical languages: a formal basis for using domain knowledge to resolve ambiguities in deep-learning based computer vision

July 2026publication date
Proposal available till 15-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Signal Processing;
Software;
Computer Vision and Pattern Recognition;
Artificial Intelligence;
Places in the authors’ list:
place 1place 2place 3place 4
FreeFreeFreeFree
2350 $1200 $1050 $900 $
Contract №5819.1 Contract №5819.2 Contract №5819.3 Contract №5819.4
1 place - free (for sale)
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)

More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
Abstract:
Human vision is able to compensate imperfections in sensory inputs from the real world by reasoning based on prior knowledge about the world. Deep learning has had a significant impact on computer vision due to its inherent ability in handling imprecision, but the absence of a reasoning framework based on domain knowledge limits its ability to interpret complex scenarios. We propose semi-lexical languages as a formal basis for reasoning with imperfect tokens provided by the real world. The power of deep learning is used to map the imperfect tokens into the alphabet of the language, and symbolic reasoning is used to determine the membership of input in the language. Semi-lexical languages have bindings that prevent the variations in which a semi-lexical token is interpreted in different parts of the input, thereby leaning on deduction to enhance the quality of recognition of individual tokens. We present case studies that demonstrate the advantage of using such a framework over pure deep learning and pure symbolic methods.
Keywords:
Explainable inference; Neuro-symbolic deduction; Semantic interpretation

Contacts :
0