Multimodal Model Performance in Zero-Shot Cross-Lingual Transfer on XTREME-R Benchmarks

This paper studies zero-shot cross-lingual transfer of vision-language models. Specifically, we focus on multilingual text-to-video search and propose a Transformer-based model that learns contextualized multilingual multimodal embeddings. Under a zero-shot setting, we empirically demonstrate that performance degrades significantly when we query the multilingual text-video model with non-English sentences. To address this problem, we introduce a multilingual multimodal pre-training strategy, and collect a new multilingual instructional video dataset (MultiHowTo100M) for pre-training. Experimen

Research goal: Do multimodal models (e.g., CLIP or OWL-ViT) with intermediate-task training on English image-text alignment tasks outperform language-only models in zero-shot cross-lingual transfer on XTREME-R benchmark tasks measured by F1-score?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.