What and Where the Themes Dominate in Image


  • Xinyu Xiao National Laboratory of Pattern Recognition
  • Lingfeng Wang Chinese Academy of Sciences
  • Shiming Xiang Chinese Academy of Sciences
  • Chunhong Pan Chinese Academy of Sciences




The image captioning is to describe an image with natural language as human, which has benefited from the advances in deep neural network and achieved substantial progress in performance. However, the perspective of human description to scene has not been fully considered in this task recently. Actually, the human description to scene is tightly related to the endogenous knowledge and the exogenous salient objects simultaneously, which implies that the content in the description is confined to the known salient objects. Inspired by this observation, this paper proposes a novel framework, which explicitly applies the known salient objects in image captioning. Under this framework, the known salient objects are served as the themes to guide the description generation. According to the property of the known salient object, a theme is composed of two components: its endogenous concept (what) and the exogenous spatial attention feature (where). Specifically, the prediction of each word is dominated by the concept and spatial attention feature of the corresponding theme in the process of caption prediction. Moreover, we introduce a novel learning method of Distinctive Learning (DL) to get more specificity of generated captions like human descriptions. It formulates two constraints in the theme learning process to encourage distinctiveness between different images. Particularly, reinforcement learning is introduced into the framework to address the exposure bias problem between the training and the testing modes. Extensive experiments on the COCO and Flickr30K datasets achieve superior results when compared with the state-of-the-art methods.




How to Cite

Xiao, X., Wang, L., Xiang, S., & Pan, C. (2019). What and Where the Themes Dominate in Image. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9021-9029. https://doi.org/10.1609/aaai.v33i01.33019021



AAAI Technical Track: Vision