Pith. sign in

REVIEW 1 cited by

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

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1711.10185 v1 pith:2KSLM56Y submitted 2017-11-28 cs.AI cs.NE

Guglielmo Montone , J. Kevin O'Regan , Alexander V. Terekhov This is my paper · ORCID
classification cs.AIcs.NE
keywords baseknowledgesystemcomputingend-to-endhyper-dimensionalpartquestions
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original 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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Augmented Vision-Language Models: A Systematic Review

    cs.CL 2025-07 conditional novelty 5.0 of 10

    A structured taxonomy of inference-time augmentation techniques that connect vision-language models to external symbolic systems, tools, and knowledge sources.

Pith tools