Zero-Shot Cross-Lingual Text-to-Image Retrieval
3 papers with code • 2 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Zero-Shot Cross-Lingual Text-to-Image Retrieval
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IGLUE
Most implemented papers
Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-training
zengyan-97/cclm
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To this end, the cross-view language modeling framework considers both multi-modal data (i. e., image-caption pairs) and multi-lingual data (i. e., parallel sentence pairs) as two different views of the same object, and trains the model to align the two views by maximizing the mutual information between them with conditional masked language modeling and contrastive learning.
Multilingual Multimodal Learning with Machine Translated Text
danoneata/td-mml
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We call this framework TD-MML: Translated Data for Multilingual Multimodal Learning, and it can be applied to any multimodal dataset and model.
Improving the Consistency in Cross-Lingual Cross-Modal Retrieval with 1-to-K Contrastive Learning
buaadreamer/ccrk
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The methods with cross-modal style suffer from the inter-modal optimization direction bias, resulting in inconsistent rank across languages within each instance, which cannot be reflected by Recall@K. To solve these problems, we propose a simple but effective 1-to-K contrastive learning method, which treats each language equally and eliminates error propagation and optimization bias.
