MGTR-MISS: More Ground Truth Retrieving based Multimodal Interaction and Semantic Supervision for video description

Describing a video with accurate words and appropriate sentence patterns is interesting and challenging. Recently, excellent models have been proposed to generate smooth and semantically rich video descriptions. However, the language generally does not participate in encoding training, and different modalities including vision and language cannot be effectively interacted and accurately aligned. In this work, a novel model named MGTR-MISS which consists of multimodal interaction and semantic supervision, is proposed to generate more accurate and semantically rich video descriptions with the help of more ground truth. In detail, more external language knowledge is firstly retrieved from the ground truth corpus in the training set to capture richer linguistic semantics for the video. Then the visual features and retrieved linguistic features are fed into a multimodal interaction module to achieve effective interaction and accurate alignment between modalities. The output multimodal representation is then fed to a caption generator for language decoding with visual-textual attention and semantic supervision mechanisms. Experimental results on the popular MSVD, MSR-VTT and VATEX datasets show that our proposed MGTR-MISS outperforms not only the baseline model but also the recent state-of-the-art methods. Particularly, the CIDEr performances reach to 111.1 and 55.0 on MSVD and MSR-VTT respectively.