Impact of Optimal Transport Distillation on Zero-Shot Cross-Lingual Image Retrieval for Low-Resource Languages in CLIP Models

Benefiting from transformer-based pre-trained language models, neural ranking models have made significant progress. More recently, the advent of multilingual pre-trained language models provides great support for designing neural cross-lingual retrieval models. However, due to unbalanced pre-training data in different languages, multilingual language models have already shown a performance gap between high and low-resource languages in many downstream tasks. And cross-lingual retrieval models built on such pre-trained models can inherit language bias, leading to suboptimal result for low-reso

Research goal: How does optimal transport distillation affect the zero-shot cross-lingual image retrieval accuracy of CLIP-based models on low-resource languages compared to standard knowledge distillation?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.4/10.