Stack-VS: Stacked Visual-Semantic Attention for Image Caption Generation
Recently, automatic image caption generation has been an important focus of the work on <italic>multimodal</italic> translation task. Existing approaches can be roughly categorized into two classes, <italic>top-down</italic> and <italic>bottom-up</italic>, the former transfers the image information (called as <italic>visual-level feature</italic>) directly into a caption, and the later uses the extracted words (called as <italic>semantic-level attribute</italic>) to generate a description. However, previous methods either are typically based one-stage decoder or partially utilize part of <italic>visual</italic>-level or <italic>semantic</italic>-level information for image caption generation. In this paper, we address the problem and propose an innovative multi-stage architecture (called as <italic>Stack-VS</italic>) for rich fine-grained image caption generation, via combining <italic>bottom-up</italic> and <italic>top-down</italic> attention models to effectively handle both <italic>visual</italic>-level and <italic>semantic</italic>-level information of an input image. Specifically, we also propose a novel well-designed stack decoder model, which is constituted by a sequence of decoder cells, each of which contains two LSTM-layers work interactively to re-optimize attention weights on both visual-level feature vectors and semantic-level attribute embeddings for generating a fine-grained image caption. Extensive experiments on the popular benchmark dataset <italic>MSCOCO</italic> show the significant improvements on different evaluation metrics, <italic>i.e.,</italic> the improvements on BLEU-4 / CIDEr / SPICE scores are 0.372, 1.226 and 0.216, respectively, as compared to the state-of-the-art.