Cross-lingual Cross-modal Pretraining Framework for Multilingual Retrieval
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Solution Overview
Problem
Current cross-lingual cross-modal models struggle with effectively learning language-invariant representations across image and text modalities, especially when limited annotated datasets are available in non-English languages, leading to performance issues in cross-lingual cross-modal applications.
Innovation Solution
A cross-lingual cross-modal pretraining framework is introduced, which uses a combination of monolingual and parallel corpora to refine the shared latent space, incorporating tasks like masked language modeling, masked region classification, cross-modal text recovery, and translation language modeling to enhance vision-language pretraining, leveraging English as a bridge for knowledge transfer to other languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine translation tools are used to translate non-English text to English for cross-lingual cross-modal applications, then the model can leverage pretrained English vision-language models, but the system suffers from high latency and performance strongly relies on the MT tool's capability
Solution Approach 1:
The system performs preliminary action by pretraining the vision-language model directly on multilingual image-caption datasets in multiple languages simultaneously, rather than translating at runtime. This advance preparation of multilingual representations eliminates the need for runtime translation operations, thereby reducing latency while maintaining cross-lingual capability.
Solution Approach 2:
The system introduces a shared latent space as an intermediary that directly connects images and text in multiple languages without requiring translation to English as an intermediate step. This mediator enables direct cross-lingual cross-modal retrieval by learning language-invariant representations that bridge different languages and modalities simultaneously.
2Measurement precision
If pretrained vision-language models are trained on large amounts of annotated image-caption datasets in English, then remarkable performance is achieved on English tasks, but the models are not directly applicable to downstream cross-modal tasks involving non-English languages
Solution Approach 1:
The system applies universality by training the vision-language model on multilingual datasets to learn universal, language-invariant representations that function across multiple languages and modalities. The shared latent space is designed to be language-agnostic, enabling the same model to perform cross-modal retrieval in English, Japanese, German, and other languages without requiring language-specific adaptations.
Solution Approach 2:
The system changes the training parameters by incorporating multilingual image-caption pairs from multiple languages simultaneously during pretraining, rather than training on English data only. This parameter change in the training data composition enables the model to learn representations that generalize across languages while maintaining high performance on all supported languages.
3Loss of information
If word-level or sentence-level alignment models are used to model images and text captions in a joint embedding space, then semantic similarity among languages and images is captured, but the models only model relevance with global features and cannot effectively detect relevance locally
Solution Approach 1:
The system applies segmentation by processing and aligning individual word tokens and their corresponding image regions separately within the joint embedding space. Instead of treating entire sentences or images as single units, the model segments the input into fine-grained components (words and regions) that can be independently aligned and matched, enabling precise local relevance detection while preserving global semantic relationships.
Data Source
AI summary
Current pretrained vision-language models for cross-modal retrieval tasks in English depend upon on the availability of many annotated image-caption datasets for pretraining to have English text. However, the texts are not necessarily in English. Although machine translation (MT) tools may be used to translate text to English, the performance largely relies on MT's quality and may suffer from high latency problems in real-world applications. Embodiments herein address these problems by learning cross-lingual cross-modal representations for matching images and their relevant captions in multiple languages. Embodiments seamlessly combine cross-lingual pretraining objectives and cross-modal pretraining objectives in a unified framework to learn image and text in a joint embedding space from available English image-caption data, monolingual corpus, and parallel corpus. Embodiments are shown to achieve state-of-the-art performance in retrieval tasks on multimodal multilingual image caption datasets.