Video Captioning with Text-based Dynamic Attention and Step-by-Step Learning

Automatically describing video content with natural language has been attracting much attention in CV and NLP communities. Most existing methods predict one word at a time, and by feeding the last generated word back as input at the next time, while the other generated words are not fully exploited. Furthermore, traditional methods optimize the model using all the training samples in each epoch without considering their learning situations, which leads to a lot of unnecessary training and can not target the difficult samples. To address these issues, we propose a text-based dynamic attention model named TDAM, which imposes a dynamic attention mechanism on all the generated words with the motivation to improve the context semantic information and enhance the overall control of the whole sentence. Moreover, the text-based dynamic attention mechanism and the visual attention mechanism are linked together to focus on the important words. They can benefit from each other during training. Accordingly, the model is trained through two steps: "starting from scratch" and "checking for gaps". The former uses all the samples to optimize the model, while the latter only trains for samples with poor control. Experimental results on the popular datasets MSVD and MSR-VTT demonstrate that our non-ensemble model outperforms the state-of-the-art video captioning benchmarks.