Hierarchical Boundary-Aware Neural Encoder for Video Captioning
This addresses the problem of generating accurate descriptions for videos, particularly in movie contexts, with incremental improvements over existing methods.
The paper tackles video captioning by proposing a recurrent encoding scheme that identifies discontinuity points to leverage hierarchical video structure, improving state-of-the-art results on movie description datasets.
The use of Recurrent Neural Networks for video captioning has recently gained a lot of attention, since they can be used both to encode the input video and to generate the corresponding description. In this paper, we present a recurrent video encoding scheme which can discover and leverage the hierarchical structure of the video. Unlike the classical encoder-decoder approach, in which a video is encoded continuously by a recurrent layer, we propose a novel LSTM cell, which can identify discontinuity points between frames or segments and modify the temporal connections of the encoding layer accordingly. We evaluate our approach on three large-scale datasets: the Montreal Video Annotation dataset, the MPII Movie Description dataset and the Microsoft Video Description Corpus. Experiments show that our approach can discover appropriate hierarchical representations of input videos and improve the state of the art results on movie description datasets.