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Is more data always better to train vision-and-language models? We studyknowledge transferability in multi-modal tasks. The current tendency in machinelearning is to assume that by joining multiple datasets from different taskstheir overall performance will improve. However, we show that not all theknowledge transfers well or has a positive impact on related tasks, even whenthey share a common goal. We conduct an exhaustive analysis based on hundredsof cross-experiments on 12 vision-and-language tasks categorized in 4 groups.Whereas tasks in the same group are prone to improve each other, results showthat this is not always the case. Other factors such as dataset size orpre-training stage have also a great impact on how well the knowledge istransferred.

Learning More May Not Be Better: Knowledge Transferability in Vision and Language Tasks