Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2022

Abstract

Supporting real-time referring expression comprehension (REC) on pervasive devices is an important capability for human-AI collaborative tasks. Model pruning techniques, applied to DNN models, can enable real-time execution even on resource-constrained devices. However, existing pruning strategies are designed principally for uni-modal applications, and suffer a significant loss of accuracy when applied to REC tasks that require fusion of textual and visual inputs. We thus present a multi-modal pruning model, LGMDP, which uses language as a pivot to dynamically and judiciously select the relevant computational blocks that need to be executed. LGMDP also introduces a new SoftSkip mechanism, whereby 'skipped' visual scales are not completely eliminated but approximated with minimal additional computation. Experimental evaluation, using 3 benchmark REC datasets and an embedded device implementation, shows that LGMDP can achieve 33% latency savings, with an accuracy loss 0.5% - 2%.

Keywords

Human-Robot Interaction, Referring Expression Comprehension, Pruning, Computer Vision, Natural Language Processing

Discipline

Computer Engineering | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

MM '22: Proceedings of the 30th ACM International Conference on Multimedia, Lisboa, October 10-14

First Page

3608

Last Page

3616

ISBN

9781450392037

Identifier

10.1145/3503161.3548432

Publisher

ACM

City or Country

New York

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/3503161.3548432

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