Multimodal LLM Enhanced Cross-lingual Cross-modal Retrieval

TL;DR

This paper introduces LECCR, a novel method that leverages multi-modal large language models to improve cross-lingual cross-modal retrieval by enhancing semantic alignment between visual and non-English textual data.

Contribution

LECCR uses multi-modal large language models to generate detailed visual descriptions and semantic slots, improving alignment between visual and non-English representations in CCR tasks.

Findings

LECCR outperforms existing methods on four CCR benchmarks.

Semantic slot aggregation enhances visual feature semantics.

Softened matching under English guidance improves cross-modal alignment.

Abstract

Cross-lingual cross-modal retrieval (CCR) aims to retrieve visually relevant content based on non-English queries, without relying on human-labeled cross-modal data pairs during training. One popular approach involves utilizing machine translation (MT) to create pseudo-parallel data pairs, establishing correspondence between visual and non-English textual data. However, aligning their representations poses challenges due to the significant semantic gap between vision and text, as well as the lower quality of non-English representations caused by pre-trained encoders and data noise. To overcome these challenges, we propose LECCR, a novel solution that incorporates the multi-modal large language model (MLLM) to improve the alignment between visual and non-English representations. Specifically, we first employ MLLM to generate detailed visual content descriptions and aggregate them into multi-view semantic slots that encapsulate different semantics. Then, we take these semantic slots as internal features and leverage them to interact with the visual features. By doing so, we enhance the semantic information within the visual features, narrowing the semantic gap between modalities and generating local visual semantics for subsequent multi-level matching. Additionally, to further enhance the alignment between visual and non-English features, we introduce softened matching under English guidance. This approach provides more comprehensive and reliable inter-modal correspondences between visual and non-English features. Extensive experiments on four CCR benchmarks, \ie Multi30K, MSCOCO, VATEX, and MSR-VTT-CN, demonstrate the effectiveness of our proposed method. Code: \url{https://github.com/LiJiaBei-7/leccr}.