Cross-Lingual Cross-Modal Knowledge Distillation for Multilingual Text-Video Retrieval
The paper "C2KD: Cross-Lingual Cross-Modal Knowledge Distillation for Multilingual Text-Video Retrieval" presents a novel framework designed to enhance multilingual text-video retrieval. This research contends with the prevalent superiority of English-based methodologies in cross-modal retrieval tasks by proposing a Cross-Lingual Cross-Modal Knowledge Distillation (C2KD) approach. The primary contribution of this work revolves around addressing the performance disparity between English and non-English text-video retrieval systems, which can be attributed to the prevalent focus on English data, both in size and quality, during pre-training and evaluation phases.
Methodological Overview
The research proposes a student-teacher framework where English scores serve as the distilled knowledge into multilingual systems. This teaching is accomplished via multiple "teacher" models that compute text-video similarity scores using English input while a separate "student" model utilizes translations of this input into various languages. The aim is to align the student's multilingual output with the superior performance metrics of the English-based teachers via a cross-modal contrastive optimization strategy.
A crucial aspect of the proposed approach is the development of a cross-entropy loss variant that facilitates the distillation from cross-modal similarity scores. This guarantees that similarity distributions generated by the student mimic those of the teachers without necessitating rigid identical match distributions. The practicality of C2KD is demonstrated through its application on datasets like Multi-YouCook2, a novel multilingual extension of YouCook2, alongside other datasets such as Multi-MSRVTT and VATEX.
Numerical Results and Implications
The implementation of C2KD yields significant improvements across a range of non-English languages, consistently narrowing the performance gap with English across multiple datasets. For instance, on the Multi-MSRVTT dataset, C2KD enhances average recall at rank 1 from 19.8 to 23.0, a notable improvement. These findings substantiate the practicality of leveraging English-centric performance to uplift multilingual counterparts, showcasing potential for more equitable retrieval solutions across languages.
The theoretical implications extend to understanding cross-modal embeddings and their alignment across languages. The method emphasizes the incorporation of cross-modal teacher-student dynamics, which could inspire further inquiry into knowledge transfer techniques and their applications beyond text-video retrieval, potentially spanning into areas such as multilingual audio-visual processing or trans-modal tasks.
Future Prospects
Future research in this domain could explore the potential of expanding this methodology to low-resource languages where high-quality machine translation may not exist, potentially integrating self-supervised learning methodologies to augment the training data. Additionally, continuing to refine the models and objectives that govern contrastive learning could lead to broader applications extending beyond multimedia retrieval to comprehensive multimodal systems. The release of the Multi-YouCook2 dataset is anticipated to spur additional multilingual multimodal research, facilitating advancements in text-video retrieval models with improved generalization capabilities.
This paper contributes substantially to the field of multilingual cross-modal retrieval by highlighting the role of knowledge distillation from high-resource languages and the potential for its application across diverse linguistic contexts.