LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval
By Siqi Sun, Yen-Chun Chen, Linjie Li, Shuohang Wang, Yuwei Fang, Jingjing Liu. 2021
94 citations recorded by the source corpus.
Multimodal pre-training has propelled great advancement in vision-and-language research. These large-scale pre-trained models, although successful, fatefully suffer from slow inference speed due to enormous computational cost mainly from cross-modal attention in Transformer architecture. When applied to real-life applications, such latency and computation demand severely deter the practical use of pre-trained models. In this paper, we study Image-text retrieval (ITR), the most mature scenario of V+L application, which has been widely studied even prior to the emergence of recent pre-trained models. We propose a simple yet highly effective approach, LightningDOT that accelerates the inference time of ITR by thousands of times, without sacrificing accuracy. LightningDOT removes the time-consuming cross-modal attention by extracting pre-cached feature indexes offline, and employing instant dot-product matching online, which significantly speeds up retrieval process. In fact, our LightningDOT achieves superior performance across mainstream ITR benchmarks such as Flickr30k and COCO datasets, outperforming existing pre-trained models that consume 1000 times magnitude of computational hours using the same features.
Subfields: Computer Vision and Pattern Recognition, Information Retrieval & Search, Multimodal Learning, Representation & Self-Supervised Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Lijuan Wang
- Zicheng Liu
- Zhe Gan
- Jianfeng Wang
- Zhengyuan Yang
- Xiaowei Hu
- Jianfeng Gao
- Cordelia Schmid
In Information Retrieval & Search
In Multimodal Learning
In Representation & Self-Supervised Learning
Citations in the corpus
- UFO: A UniFied TransfOrmer for Vision-Language Representation Learning (2021, background): “Beyond the grid-level representation, LightningDOT [41] explores the region-level features through a Faster RCNN [36] 1As of 11/2021 among peer-reviewed publications. network to obtain the image representation.”
- Cross-Modal Implicit Relation Reasoning and Aligning for Text-to-Image Person Retrieval (2023, background): “text description query [30], which is a sub-task of both image-text retrieval [26,33,42] and image-based person reidentification (Re-ID) [15,32,45].”