We propose a high-level concept word detector that can be integrated with any video-to-language models. It takes a video as input and generates a list of concept words as useful semantic priors for language generation models. The proposed word detector has two important properties. First, it does not require any external knowledge sources for training. Second, the proposed word detector is trainable in an end-to-end manner jointly with any video-to-language models. To effectively exploit the detected words, we also develop a semantic attention mechanism that selectively focuses on the detected concept words and fuse them with the word encoding and decoding in the language model. In order to demonstrate that the proposed approach indeed improves the performance of multiple video-to-language tasks, we participate in all the four tasks of LSMDC 2016 . Our approach has won three of them, including fill-in-theblank, multiple-choice test, and movie retrieval.
|Title of host publication||Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||9|
|Publication status||Published - 2017 Nov 6|
|Event||30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 - Honolulu, United States|
Duration: 2017 Jul 21 → 2017 Jul 26
|Name||Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017|
|Other||30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017|
|Period||17/7/21 → 17/7/26|
Bibliographical notePublisher Copyright:
© 2017 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition