# Hyper-dimensional computing for a visual question-answering system that is trainable end-to-end

Source: [Hyper-dimensional computing for a visual question-answering system that is trainable end-to-end](<https://www.sota2.com/research/paper/68fa63d81e209131cea013bc>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:51:10.308Z

Authors: Guglielmo Montone, J. Kevin O'Regan, Alexander V. Terekhov

Published: 2017-11-28T09:00:55.000Z

## Abstract

In this work we propose a system for visual question answering. Our architecture is composed of two parts, the first part creates the logical knowledge base given the image. The second part evaluates questions against the knowledge base. Differently from previous work, the knowledge base is represented using hyper-dimensional computing. This choice has the advantage that all the operations in the system, namely creating the knowledge base and evaluating the questions against it, are differentiable, thereby making the system easily trainable in an end-to-end fashion.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1711.10185>)

[PDF](<https://arxiv.org/pdf/1711.10185.pdf>)