Vector Semiotic Model for Visual Question Answering |
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Affiliation: | 1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, No.95 Zhongguancun East Road, 100190, Beijing, China;2. University of Chinese Academy of Sciences, Beijing, China;3. Intelligent Advertising Lab, Business Growth BU, JD.com, China |
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Abstract: | In this paper, we propose a Vector Semiotic Model as a possible solution to the symbol grounding problem in the context of Visual Question Answering. The Vector Semiotic Model combines the advantages of a Semiotic Approach implemented in the Sign-Based World Model and Vector Symbolic Architectures. The Sign-Based World Model represents information about a scene depicted on an input image in a structured way and grounds abstract objects in an agent’s sensory input. We use the Vector Symbolic Architecture to represent the elements of the Sign-Based World Model on a computational level. Properties of a high-dimensional space and operations defined for high-dimensional vectors allow encoding the whole scene into a high-dimensional vector with the preservation of the structure. That leads to the ability to apply explainable reasoning to answer an input question. We conducted experiments are on a CLEVR dataset and show results comparable to the state of the art. The proposed combination of approaches, first, leads to the possible solution of the symbol-grounding problem and, second, allows expanding current results to other intelligent tasks (collaborative robotics, embodied intellectual assistance, etc.). |
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Keywords: | Vector-symbolic architecture Semiotic approach Symbol grounding problem Causal network Visual Question Answering |
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