2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
Login Paper Search My Schedule Paper Index Help

My ICASSP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDMMSP-7.3
Paper Title ECCL: EXPLICIT CORRELATION-BASED CONVOLUTION BOUNDARY LOCATOR FOR MOMENT LOCALIZATION
Authors Xinfang Liu, Shandong University, China; Xiushan Nie, Shandong Jianzhu University, China; Junya Teng, Shandong University, China; Fanchang Hao, Shandong Jianzhu University, China; Yilong Yin, Shandong University, China
SessionMMSP-7: Multimodal Perception, Integration and Multisensory Fusion
LocationGather.Town
Session Time:Friday, 11 June, 13:00 - 13:45
Presentation Time:Friday, 11 June, 13:00 - 13:45
Presentation Poster
Topic Multimedia Signal Processing: Human Centric Multimedia
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Moment localization in videos using natural language refers to finding the most relevant segment from the video with given a query in natural language form. In this paper, we present a new boundary-determining strategy called explicit correlation-based convolution boundary locator (ECCL), which can handle any lengths of videos and moments while leveraging fine-grained matching relationships. In this method, we first train a deep network to obtain the correlation scores between video clips and query statements. Subsequently, with the correlation scores, we utilize a convolution kernel to generate the boundary probability distribution. Finally, the start and end time indexes of the video moment are calculated with an optimization problem. Experiments on two publicly available datasets demonstrate the feasibility of ECCL.