While Vector Symbolic Architectures (VSAs) are promising for modelling
spatial cognition, their application is currently limited to artificially
generated images and simple spatial queries. We propose VSA4VQA - a novel 4D
implementation of VSAs that implements a mental representation of natural
images for the challenging task of Visual Question Answering (VQA). VSA4VQA is
the first model to scale a VSA to complex spatial queries. Our method is based
on the Semantic Pointer Architecture (SPA) to encode objects in a
hyperdimensional vector space. To encode natural images, we extend the SPA to
include dimensions for object's width and height in addition to their spatial
location. To perform spatial queries we further introduce learned spatial query
masks and integrate a pre-trained vision-language model for answering
attribute-related questions. We evaluate our method on the GQA benchmark
dataset and show that it can effectively encode natural images, achieving
competitive performance to state-of-the-art deep learning methods for zero-shot
VQA.

提出了一种名为 VSA4VQA 的新型 VSAs 的实施，它是第一个将 VSA 扩展到复杂空间查询的模型，并通过引入学习的空间查询掩模和预训练的视觉 - 语言模型来解决视觉问题回答任务，评估结果表明它能够有效地编码自然图像，并取得与最先进的深度学习方法竞争性的性能。