Synchronized Audio-Visual Frames with Fractional Positional Encoding for Transformers in Video-to-Text Translation

• URL: http://arxiv.org/abs/2112.14088v1
• Date: Tue, 28 Dec 2021 10:57:18 GMT
• Title: Synchronized Audio-Visual Frames with Fractional Positional Encoding for Transformers in Video-to-Text Translation
• Authors: Philipp Harzig, Moritz Einfalt, Rainer Lienhart
• Abstract summary: Video-to-Text (VTT) is the task of automatically generating descriptions for short audio-visual video clips. Transformers have shown great performance in both machine translation and image captioning, lacking a straightforward and reproducible application for VTT. We explore promising approaches from image captioning and video processing and apply them to VTT by developing a straightforward Transformer architecture.
• Score: 26.36252496316238
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video-to-Text (VTT) is the task of automatically generating descriptions for short audio-visual video clips, which can support visually impaired people to understand scenes of a YouTube video for instance. Transformer architectures have shown great performance in both machine translation and image captioning, lacking a straightforward and reproducible application for VTT. However, there is no comprehensive study on different strategies and advice for video description generation including exploiting the accompanying audio with fully self-attentive networks. Thus, we explore promising approaches from image captioning and video processing and apply them to VTT by developing a straightforward Transformer architecture. Additionally, we present a novel way of synchronizing audio and video features in Transformers which we call Fractional Positional Encoding (FPE). We run multiple experiments on the VATEX dataset to determine a configuration applicable to unseen datasets that helps describe short video clips in natural language and improved the CIDEr and BLEU-4 scores by 37.13 and 12.83 points compared to a vanilla Transformer network and achieve state-of-the-art results on the MSR-VTT and MSVD datasets. Also, FPE helps increase the CIDEr score by a relative factor of 8.6%.

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