  We present a new state-of-the-art on the text to video retrieval task on
MSRVTT and LSMDC benchmarks where our model outperforms all previous solutions
by a large margin. Moreover, state-of-the-art results are achieved with a
single model on two datasets without finetuning. This multidomain
generalisation is achieved by a proper combination of different video caption
datasets. We show that training on different datasets can improve test results
of each other. Additionally we check intersection between many popular datasets
and found that MSRVTT has a significant overlap between the test and the train
parts, and the same situation is observed for ActivityNet.
