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We present a method for matching a text sentence from a given corpus to agiven video clip and vice versa. Traditionally video and text matching is doneby learning a shared embedding space and the encoding of one modality isindependent of the other. In this work, we encode the dataset data in a waythat takes into account the query's relevant information. The power of themethod is demonstrated to arise from pooling the interaction data between wordsand frames. Since the encoding of the video clip depends on the sentencecompared to it, the representation needs to be recomputed for each potentialmatch. To this end, we propose an efficient shallow neural network. Itstraining employs a hierarchical triplet loss that is extendable toparagraph/video matching. The method is simple, provides explainability, andachieves state-of-the-art results for both sentence-clip and video-text by asizable margin across five different datasets: ActivityNet, DiDeMo, YouCook2,MSR-VTT, and LSMDC. We also show that our conditioned representation can betransferred to video-guided machine translation, where we improved the currentresults on VATEX. Source code is available athttps://github.com/AmeenAli/VideoMatch.

Video and Text Matching with Conditioned Embeddings