NAACL 2022long6 citations
Leaner and Faster: Two-Stage Model Compression for Lightweight Text-Image Retrieval
Abstract
Current text-image approaches (e.g., CLIP) typically adopt dual-encoder architecture using pre-trained vision-language representation. However, these models still pose non-trivial memory requirements and substantial incremental indexing time, which makes them less practical on mobile devices. In this paper, we present an effective two-stage framework to compress large pre-trained dual-encoder for lightweight text-image retrieval. The resulting model is smaller (39% of the original), faster (1.6x/2.9x for processing image/text respectively), yet performs on par with or better than the original full model on Flickr30K and MSCOCO benchmarks. We also open-source an accompanying realistic mobile image search application.
BibTeX
@inproceedings{ren-zhu-2022-leaner,
title = "Leaner and Faster: Two-Stage Model Compression for Lightweight Text-Image Retrieval",
author = "Ren, Siyu and
Zhu, Kenny",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.300/",
doi = "10.18653/v1/2022.naacl-main.300",
pages = "4085--4090"
}