SoftSkip: Empowering Multi-Modal Dynamic Pruning for Single-Stage Referring Comprehension
Published in Proceedings of the 30th ACM International Conference on Multimedia, 2022
Recommended citation: Weerakoon, D., Subbaraju, V., Tran, T. and Misra, A., 2022, October. SoftSkip: Empowering Multi-Modal Dynamic Pruning for Single-Stage Referring Comprehension. In Proceedings of the 30th ACM International Conference on Multimedia (pp. 3608-3616).
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%