VLDeformer: Vision-Language Decomposed Transformer for Fast Cross-Modal Retrieval
This addresses the problem of slow inference in cross-modal search engines for real-world applications, representing an incremental improvement in efficiency.
The paper tackles the inefficiency of vision-language transformers in cross-modal retrieval by proposing VLDeformer, which separates the process into learning and decomposition stages, achieving over 1000x acceleration with less than 0.6% average recall drop and outperforming state-of-the-art methods on benchmarks like COCO and Flickr30k.
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.