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Boosting Video Captioning with Dynamic Loss Network

2021-07-25 01:32:02
Nasibullah, Partha Pratim Mohanta
arXiv_CV
                     
Abstract

Video captioning is one of the challenging problems at the intersection of vision and language, having many real-life applications in video retrieval, video surveillance, assisting visually challenged people, Human-machine interface, and many more. Recent deep learning-based methods have shown promising results but are still on the lower side than other vision tasks (such as image classification, object detection). A significant drawback with existing video captioning methods is that they are optimized over cross-entropy loss function, which is uncorrelated to the de facto evaluation metrics (BLEU, METEOR, CIDER, ROUGE).In other words, cross-entropy is not a proper surrogate of the true loss function for video captioning. This paper addresses the drawback by introducing a dynamic loss network (DLN), which provides an additional feedback signal that directly reflects the evaluation metrics. Our results on Microsoft Research Video Description Corpus (MSVD) and MSR-Video to Text (MSRVTT) datasets outperform previous methods.

Abstract (translated)

URL

https://arxiv.org/abs/2107.11707

PDF

https://arxiv.org/pdf/2107.11707.pdf

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