Rich Visual and Language Representation with Complementary Semantics for Video Captioning

It is interesting and challenging to translate a video to natural description sentences based on the video content. In this work, an advanced framework is built to generate sentences with coherence and rich semantic expressions for video captioning. A long short term memory (LSTM) network with an improved factored way is first developed, which takes the inspiration of LSTM with a conventional factored way and a common practice to feed multi-modal features into LSTM at the first time step for visual description. Then, the incorporation of the LSTM network with the proposed improved factored way and un-factored way is exploited, and a voting strategy is utilized to predict candidate words. In addition, for robust and abstract visual and language representation, residuals are employed to enhance the gradient signals that are learned from the residual network (ResNet), and a deeper LSTM network is constructed. Furthermore, three convolutional neural network based features extracted from GoogLeNet, ResNet101, and ResNet152, are fused to catch more comprehensive and complementary visual information. Experiments are conducted on two benchmark datasets, including MSVD and MSR-VTT2016, and competitive performances are obtained by the proposed techniques as compared to other state-of-the-art methods.