Details

Autor(en) / Beteiligte

• Zhe Gan
• Chuang Gan
• Xiaodong He
• Yunchen Pu
• Tran, Kenneth
• Jianfeng Gao
• Carin, Lawrence
• Li Deng

Titel

Semantic Compositional Networks for Visual Captioning

Ist Teil von

• 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, p.1141-1150

Ort / Verlag

IEEE

Erscheinungsjahr

2017

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• A Semantic Compositional Network (SCN) is developed for image captioning, in which semantic concepts (i.e., tags) are detected from the image, and the probability of each tag is used to compose the parameters in a long short-term memory (LSTM) network. The SCN extends each weight matrix of the LSTM to an ensemble of tag-dependent weight matrices. The degree to which each member of the ensemble is used to generate an image caption is tied to the image-dependent probability of the corresponding tag. In addition to captioning images, we also extend the SCN to generate captions for video clips. We qualitatively analyze semantic composition in SCNs, and quantitatively evaluate the algorithm on three benchmark datasets: COCO, Flickr30k, and Youtube2Text. Experimental results show that the proposed method significantly outperforms prior state-of-the-art approaches, across multiple evaluation metrics.