TR2019-048

Audio-Visual Scene-Aware Dialog


    •  Alamri, H., Cartillier, V., Das, A., Wang, J., Lee, S., Anderson, P., Essa, I., Parikh, D., Batra, D., Cherian, A., Marks, T.K., Hori, C., "Audio-Visual Scene-Aware Dialog", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), DOI: 10.1109/​CVPR.2019.00774, June 2019, pp. 7550-7559.
      BibTeX TR2019-048 PDF
      • @inproceedings{Alamri2019jun,
      • author = {Alamri, Huda and Cartillier, Vincent and Das, Abhishek and Wang, Jue and Lee, Stefan and Anderson, Peter and Essa, Irfan and Parikh, Devi and Batra, Dhruv and Cherian, Anoop and Marks, Tim K. and Hori, Chiori},
      • title = {Audio-Visual Scene-Aware Dialog},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2019,
      • pages = {7550--7559},
      • month = jun,
      • doi = {10.1109/CVPR.2019.00774},
      • url = {https://www.merl.com/publications/TR2019-048}
      • }
  • MERL Contacts:
  • Research Areas:

    Artificial Intelligence, Computer Vision, Machine Learning, Speech & Audio

Abstract:

We introduce the task of scene-aware dialog. Given a follow-up question in an ongoing dialog about a video, our goal is to generate a complete and natural response to a question given (a) an input video, and (b) the history of previous turns in the dialog. To succeed, agents must ground the semantics in the video and leverage contextual cues from the history of the dialog to answer the question. To benchmark this task, we introduce the Audio Visual Scene-Aware Dialog (AVSD) dataset. For each of more than 11,000 videos of human actions for the Charades dataset. Our dataset contains a dialog about the video, plus a final summary of the video by one of the dialog participants. We train several baseline systems for this task and evaluate the performance of the trained models using several qualitative and quantitative metrics. Our results indicate that the models must comprehend all the available inputs (video, audio, question and dialog history) to perform well on this dataset.

 

  • Related News & Events

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      Date: March 1, 2022
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        IEEE Spectrum is the flagship magazine and website of the IEEE, the world’s largest professional organization devoted to engineering and the applied sciences. IEEE Spectrum has a circulation of over 400,000 engineers worldwide, making it one of the leading science and engineering magazines.
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      MERL Contact: Chiori Hori
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      Date: July 22, 2020
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        Scene-Aware Interaction for car navigation, one target application, will provide drivers with intuitive route guidance. The technology is also expected to have applicability to human-machine interfaces for in-vehicle infotainment, interaction with service robots in building and factory automation systems, systems that monitor the health and well-being of people, surveillance systems that interpret complex scenes for humans and encourage social distancing, support for touchless operation of equipment in public areas, and much more. The technology is based on recent research by MERL's Speech & Audio and Computer Vision groups.
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