Dual-Stream Recurrent Neural Network for Video Captioning

Abstract

Recent progress in using recurrent neural networks (RNNs) for video description has attracted an increasing interest, due to its capability to encode a sequence of frames for caption generation. While existing methods have studied various features (e.g., CNN, 3D CNN, and semantic attributes) for visual encoding, the representation and fusion of heterogeneous information from multi-modal spaces have not fully explored. Consider that different modalities are often asynchronous, frame-level multi-modal fusion (e.g., concatenation and linear fusion) will negatively influence each modality. In this paper, we propose a dual-stream RNN (DS-RNN) framework to jointly discover and integrate the hidden states of both visual and semantic streams for video caption generation. First, an encoding RNN is used for each stream to flexibly exploit the hidden states of respective modality. Specifically, we proposed an attentive multi-grained encoder module to enhance the local feature learning with global semantics feature. Then, a dual-stream decoder is deployed to integrate the asynchronous yet complementary sequential hidden states from both streams for caption generation. Extensive experiments on three benchmark datasets, namely, MSVD, MSR-VTT, and MPII-MD, show that DS-RNN achieves competitive performance against the state-of-the-art. Additional ablation studies were conducted on various variants of the proposed DS-RNN.