Semantically Sensible Video Captioning (SSVC)
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Video captioning, i.e. the task of generating captions from video sequences creates a bridge between the Natural Language Processing and Computer Vision domains of computer science. Generating a semantically accurate description of a video is an arduous task. Considering the complexity of the problem, the results obtained in recent researches are quite outstanding. But still there is plenty of scope for improvement. This paper addresses this scope and proposes a novel solution. Most video captioning models comprise of two sequential/recurrent layers - one as a video-to-context encoder and the other as a context-to-caption decoder. This paper proposes a novel architecture, SSVC (Semantically Sensible Video Captioning) which modifies the context generation mechanism by using two novel approaches - "stacked attention" and "spatial hard pull". For evaluating the proposed architecture, along with the BLEU scoring metric for quantitative analysis, we have used a human evaluation metric for qualitative analysis. This paper refers to this proposed human evaluation metric as the Semantic Sensibility (SS) scoring metric. SS score overcomes the shortcomings of common automated scoring metrics. This paper reports that the use of the aforementioned novelties improves the performance of the state-of-the-art architectures.
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