VLDeformer: Vision-Language Decomposed Transformer for Fast Cross-Modal Retrieval

TL;DR

VLDeformer introduces a novel approach to vision-language retrieval by decomposing a transformer into separate stages, significantly boosting efficiency while maintaining high accuracy, making it suitable for real-time cross-modal search engines.

Contribution

The paper proposes VLDeformer, a decomposed transformer architecture that separates cross-modal retrieval into learning and indexing stages, greatly improving efficiency with minimal accuracy loss.

Findings

Achieves over 1000x speedup in retrieval tasks.

Maintains less than 0.6% recall drop after decomposition.

Outperforms state-of-the-art methods on COCO and Flickr30k datasets.

Abstract

Cross-model retrieval has emerged as one of the most important upgrades for text-only search engines (SE). Recently, with powerful representation for pairwise text-image inputs via early interaction, the accuracy of vision-language (VL) transformers has outperformed existing methods for text-image retrieval. However, when the same paradigm is used for inference, the efficiency of the VL transformers is still too low to be applied in a real cross-modal SE. Inspired by the mechanism of human learning and using cross-modal knowledge, this paper presents a novel Vision-Language Decomposed Transformer (VLDeformer), which greatly increases the efficiency of VL transformers while maintaining their outstanding accuracy. By the proposed method, the cross-model retrieval is separated into two stages: the VL transformer learning stage, and the VL decomposition stage. The latter stage plays the role of single modal indexing, which is to some extent like the term indexing of a text SE. The model learns cross-modal knowledge from early-interaction pre-training and is then decomposed into an individual encoder. The decomposition requires only small target datasets for supervision and achieves both $1000+$ times acceleration and less than $0.6$\% average recall drop. VLDeformer also outperforms state-of-the-art visual-semantic embedding methods on COCO and Flickr30k.