TRIPS: Efficient Vision-and-Language Pre-training with Text-Relevant Image Patch Selection

Chaoya Jiang, Haiyang Xu, Chenliang Li, Ming Yan, Wei Ye, Shikun Zhang, Bin Bi, Songfang Huang

Abstract
Vision Transformers (ViTs) have been widely used in large-scale Vision and Language Pre-training (VLP) models. Though previous VLP works have proved the effectiveness of ViTs, they still suffer from computational efficiency brought by the long visual sequence. To tackle this problem, in this paper, we propose an efficient vision-and-language pre-training model with Text-Relevant Image Patch Selection, namely TRIPS, which reduces the visual sequence progressively with a text-guided patch-selection layer in the visual backbone for efficient training and inference. The patch-selection layer can dynamically compute text-dependent visual attention to identify the attentive image tokens with text guidance and fuse inattentive ones in an end-to-end manner. Meanwhile, TRIPS does not introduce extra parameters to ViTs. Experimental results on a variety of popular benchmark datasets demonstrate that TRIPS gain a speedup of 40% over previous similar VLP models, yet with competitive or better downstream task performance.
Anthology ID:
2022.emnlp-main.273
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4084–4096
Language:
URL:
https://aclanthology.org/2022.emnlp-main.273
DOI:
Bibkey:
Cite (ACL):
Chaoya Jiang, Haiyang Xu, Chenliang Li, Ming Yan, Wei Ye, Shikun Zhang, Bin Bi, and Songfang Huang. 2022. TRIPS: Efficient Vision-and-Language Pre-training with Text-Relevant Image Patch Selection. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 4084–4096, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
TRIPS: Efficient Vision-and-Language Pre-training with Text-Relevant Image Patch Selection (Jiang et al., EMNLP 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.emnlp-main.273.pdf
PDF Search