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We address the problem of language-based temporal localization of moments inuntrimmed videos. Compared to temporal localization with fixed categories, thisproblem is more challenging as the language-based queries have no predefinedactivity classes and may also contain complex descriptions. Currentstate-of-the-art model MAC addresses it by mining activity concepts from bothvideo and language modalities. This method encodes the semantic activityconcepts from the verb/object pair in a language query and leverages visualactivity concepts from video activity classification prediction scores. Wepropose "Multi-faceted VideoMoment Localizer" (MML), an extension of MAC modelby the introduction of visual object evidence via object segmentation masks andvideo understanding features via video captioning. Furthermore, we improvelanguage modelling in sentence embedding. We experimented on Charades-STAdataset and identified that MML outperforms MAC baseline by 4.93% and 1.70% onR@1 and R@5metrics respectively. Our code and pre-trained model are publiclyavailable at https://github.com/madhawav/MML.

Video Moment Localization using Object Evidence and Reverse Captioning