It is well believed that video captioning is a fundamental but challenging
task in both computer vision and artificial intelligence fields. The prevalent
approach is to map an input video to a variable-length output sentence in a
sequence to sequence manner via Recurrent Neural Network (RNN). Nevertheless,
the training of RNN still suffers to some degree from vanishing/exploding
gradient problem, making the optimization difficult. Moreover, the inherently
recurrent dependency in RNN prevents parallelization within a sequence during
training and therefore limits the computations. In this paper, we present a
novel design --- Temporal Deformable Convolutional Encoder-Decoder Networks
(dubbed as TDConvED) that fully employ convolutions in both encoder and decoder
networks for video captioning. Technically, we exploit convolutional block
structures that compute intermediate states of a fixed number of inputs and
stack several blocks to capture long-term relationships. The structure in
encoder is further equipped with temporal deformable convolution to enable
free-form deformation of temporal sampling. Our model also capitalizes on
temporal attention mechanism for sentence generation. Extensive experiments are
conducted on both MSVD and MSR-VTT video captioning datasets, and superior
results are reported when comparing to conventional RNN-based encoder-decoder
techniques. More remarkably, TDConvED increases CIDEr-D performance from 58.8%
to 67.2% on MSVD.

本文提出了一种新颖的设计，即 TDConvED，它在视频字幕生成中充分利用编码器和解码器网络中的卷积，具有卷积块结构，在编码器中进一步配备时间变形卷积以实现时间采样的自由形变，并利用时间关注机制进行句子生成，在 MSVD 和 MSR-VTT 视频字幕生成数据集上进行了广泛实验，在与常规基于 RNN 的编码器解码器技术进行比较时获得更好的结果。