mlsanityData to 7 Oct 2026

MDMMT-2: Multidomain Multimodal Transformer for Video Retrieval, One More Step Towards Generalization

arXiv 2203.07086 ↗First posted 14 Mar 2022cs.CVcited by 10

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Abstract (arXiv)

In this work we present a new State-of-The-Art on the text-to-video retrieval task on MSR-VTT, LSMDC, MSVD, YouCook2 and TGIF obtained by a single model. Three different data sources are combined: weakly-supervised videos, crowd-labeled text-image pairs and text-video pairs. A careful analysis of available pre-trained networks helps to choose the best prior-knowledge ones. We introduce three-stage training procedure that provides high transfer knowledge efficiency and allows to use noisy datasets during training without prior knowledge degradation. Additionally, double positional encoding is used for better fusion of different modalities and a simple method for non-square inputs processing is suggested.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 39 of its 51 references that are in the map. First posted on arXiv 14 Mar 2022.

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39 of 51 references.

References · 39

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27 more
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Citing papers per year: 2022: 4; 2023: 3; 2024: 1; 2025: 2.

10 papers.