Hierarchical Boundary-Aware Neural Encoder for Video Captioning

Lorenzo Baraldi, Costantino Grana, Rita Cucchiara; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 1657-1666

Abstract


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.

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[bibtex]
@InProceedings{Baraldi_2017_CVPR,
author = {Baraldi, Lorenzo and Grana, Costantino and Cucchiara, Rita},
title = {Hierarchical Boundary-Aware Neural Encoder for Video Captioning},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}