ACTION RECOGNITION: FIRST-AND SECOND-ORDER 3D FEATURE IN BI-DIRECTIONAL ATTENTION NETWORK
- Authors
- Kwon, Oh Chul; Kim, Junyeong; Yoo, Chang D.
- Issue Date
- Oct-2018
- Publisher
- IEEE
- Keywords
- C3D; bi-directional LSTM; Attention; spatio-temporal bi-directional LSTM Attention
- Citation
- 2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), pp 3453 - 3457
- Pages
- 5
- Journal Title
- 2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
- Start Page
- 3453
- End Page
- 3457
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/63833
- DOI
- 10.1109/ICIP.2018.8451493
- ISSN
- 1522-4880
- Abstract
- This paper considers a 3D convolutional neural network (CNN) that learns spatial and temporal regions of higher importance through a bi-direction long short-term memory (bi-LSTM) attention for action recognition. First- and second-order differences of spatially most relevant C3D features (sp-m-C3D) are obtained, and the concatenation of the two differences with the sp-m-C3D is used to generate a temporal attention on the sp-m-C3D using a bi-LSTM. Temporally most relevant sp-m-C3D features are fed into another bi-LSTM for action recognition. Essentially, the network learns spatial and temporal regions of high importance for action recognition. We evaluate the network on two public action recognition datasets: UCF-101 (YouTube Action) and HMDB51. The proposed network performs better compared to other state-of-the-art networks.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Software > Department of Artificial Intelligence > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/63833)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.