Papers by Cordelia Schmid
Towards Zero-Shot Multimodal Machine Translation (2025.findings-naacl)
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| Challenge: | Current multimodal machine translation systems rely on fully supervised data, which is costly to collect and prevents extension of MMT to language pairs with no such data. |
| Approach: | They propose a method to bypass the need for fully supervised data to train MMT systems . they adapt a strong text-only machine translation model to a visually conditioned language model and a divergence test set to evaluate how well models use images to disambiguate translations. |
| Outcome: | The proposed method can generalize to languages with no fully supervised training data. |
Modular Visual Question Answering via Code Generation (2023.acl-short)
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Sanjay Subramanian, Medhini Narasimhan, Kushal Khangaonkar, Kevin Yang, Arsha Nagrani, Cordelia Schmid, Andy Zeng, Trevor Darrell, Dan Klein
| Challenge: | a framework for visual question answering is based on modular code generation . the scope of reasoning needed for visual questions is vast, and requires many skills . |
| Approach: | They propose a framework that formulates visual question answering as modular code generation. |
| Outcome: | The proposed framework improves accuracy on COVR and GQA datasets by 3% and 2% compared to the few-shot baseline that does not employ code generation. |
mOSCAR: A Large-scale Multilingual and Multimodal Document-level Corpus (2025.findings-acl)
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Matthieu Futeral, Armel Randy Zebaze, Pedro Ortiz Suarez, Julien Abadji, Rémi Lacroix, Cordelia Schmid, Rachel Bawden, Benoît Sagot
| Challenge: | Existing studies show that multimodal large language models can learn from text-image data. |
| Approach: | They propose to train multimodal large language models on large amounts of text-image data . they also show a boost in few-shot learning performance across various multilingual tasks . |
| Outcome: | The proposed dataset is not public and is only in English . it is the first large-scale multilingual and multimodal document corpus crawled from the web. |
OVFact: Measuring and Improving Open-Vocabulary Factuality for Long Caption Models (2025.findings-emnlp)
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| Challenge: | Large vision-language models struggle to generate long and factual captions . traditional measures for hallucination and factuality are not well suited for longer captions. |
| Approach: | They propose a method for measuring caption factuality of long captions that leverages open-vocabulary visual grounding and tool-based verification without relying on human annotations. |
| Outcome: | The proposed method improves agreement with human judgements and captures both caption descriptiveness and factual precision in the same metric. |
Tackling Ambiguity with Images: Improved Multimodal Machine Translation and Contrastive Evaluation (2023.acl-long)
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| Challenge: | Recent work in multimodal machine translation (MT) has shown that ambiguity can be resolved using accompanying context such as images. |
| Approach: | They propose a multimodal machine translation approach based on a strong text-only MT model and a novel guided self-attention mechanism to train it. |
| Outcome: | The proposed model outperforms existing models on EnglishFrench, EnglishGerman and EnglishCzech benchmarks and is freely available. |