TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Optimizing the Prediction Path through Dependency Fusion |
| 영문제목(English Title) |
Optimizing the Prediction Path through Dependency Fusion |
| 저자(Author) |
Fangxin Wang
Jie Liu
Shuwu Zhang
Guixuan Zhang
Yang Zheng
Xiaoqian Li
Wei Liang
Yuejun Li
|
| 원문수록처(Citation) |
VOL 13 NO. 09 PP. 4665 ~ 4683 (2019. 09) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
Previous methods build image annotation model by leveraging three basic dependencies: relations between image and label (image/label), between images (image/image) and between labels (label/label). Even though plenty of researches show that multiple dependencies can work jointly to improve annotation performance, different dependencies actually do not "work jointly" in their diagram, whose performance is largely depending on the result predicted by image/label section. To address this problem, we propose the adaptive attention annotation model (AAAM) to associate these dependencies with the prediction path, which is composed of a series of labels (tags) in the order they are detected. In particular, we optimize the prediction path by detecting the relevant labels from the easy-to-detect to the hard-to-detect, which are found using Binary Cross-Entropy (BCE) and Triplet Margin (TM) losses, respectively. Besides, in order to capture the inforamtion of each label, instead of explicitly extracting regional featutres, we propose the self-attention machanism to implicitly enhance the relevant region and restrain those irrelevant. To validate the effective of the model, we conduct experiments on three well-known public datasets, COCO 2014, IAPR TC-12 and NUSWIDE, and achieve better performance than the state-of-the-art methods.
|
| 키워드(Keyword) |
image annotation
multiple dependencies
self-attention
prediction path
Triplet Margin loss
|
| 파일첨부 |
PDF 다운로드
|
