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Deep set prediction networks

Deep set prediction networks
Deep set prediction networks
Current approaches for predicting sets from feature vectors ignore the unordered nature of sets and suffer from discontinuity issues as a result. We propose a general model for predicting sets that properly respects the structure of sets and avoids this problem. With a single feature vector as input, we show that our model is able to auto-encode point sets, predict the set of bounding boxes of objects in an image, and predict the set of attributes of these objects.
set prediction, deep learning
1-11
Zhang, Yan
0edf84ab-1e32-4239-bef6-7fe80d6bc7a7
Hare, Jonathon
65ba2cda-eaaf-4767-a325-cd845504e5a9
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e
Zhang, Yan
0edf84ab-1e32-4239-bef6-7fe80d6bc7a7
Hare, Jonathon
65ba2cda-eaaf-4767-a325-cd845504e5a9
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e

Zhang, Yan, Hare, Jonathon and Prugel-Bennett, Adam (2019) Deep set prediction networks. 2019 Conference on Neural Information Processing Systems, Vancouver Convention Center, Vancouver, Canada. 08 - 14 May 2019. pp. 1-11 .

Record type: Conference or Workshop Item (Paper)

Abstract

Current approaches for predicting sets from feature vectors ignore the unordered nature of sets and suffer from discontinuity issues as a result. We propose a general model for predicting sets that properly respects the structure of sets and avoids this problem. With a single feature vector as input, we show that our model is able to auto-encode point sets, predict the set of bounding boxes of objects in an image, and predict the set of attributes of these objects.

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Accepted/In Press date: 4 September 2019
Published date: 8 December 2019
Venue - Dates: 2019 Conference on Neural Information Processing Systems, Vancouver Convention Center, Vancouver, Canada, 2019-05-08 - 2019-05-14
Keywords: set prediction, deep learning

Identifiers

Local EPrints ID: 434826
URI: http://eprints.soton.ac.uk/id/eprint/434826
PURE UUID: 9daf45d4-018c-4fad-af98-0f70b40204b4
ORCID for Yan Zhang: ORCID iD orcid.org/0000-0003-3470-3663
ORCID for Jonathon Hare: ORCID iD orcid.org/0000-0003-2921-4283

Catalogue record

Date deposited: 11 Oct 2019 16:30
Last modified: 27 Feb 2021 02:49

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Contributors

Author: Yan Zhang ORCID iD
Author: Jonathon Hare ORCID iD
Author: Adam Prugel-Bennett

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