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Large-scale noisy web image-text datasets have been proven to be efficientfor learning robust vision-language models. However, when transferring them tothe task of video retrieval, models still need to be fine-tuned on hand-curatedpaired text-video data to adapt to the diverse styles of video descriptions. Toaddress this problem without the need for hand-annotated pairs, we propose anew setting, text-video retrieval with uncurated & unpaired data, that duringtraining utilizes only text queries together with uncurated web videos withoutany paired text-video data. To this end, we propose an approach, In-Style, thatlearns the style of the text queries and transfers it to uncurated web videos.Moreover, to improve generalization, we show that one model can be trained withmultiple text styles. To this end, we introduce a multi-style contrastivetraining procedure that improves the generalizability over several datasetssimultaneously. We evaluate our model on retrieval performance over multipledatasets to demonstrate the advantages of our style transfer framework on thenew task of uncurated & unpaired text-video retrieval and improvestate-of-the-art performance on zero-shot text-video retrieval.

In-Style: Bridging Text and Uncurated Videos with Style Transfer for Text-Video Retrieval