Video Alignment Using Bi-Directional Attention Flow in a Multi-Stage Learning Model

Reham Abobeah*, Amin Shoukry, Jiro Katto

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Recently, deep learning techniques have contributed to solving a multitude of computer vision tasks. In this paper, we propose a deep-learning approach for video alignment, which involves finding the best correspondences between two overlapping videos. We formulate the video alignment task as a variant of the well-known machine comprehension (MC) task in natural language processing. While MC answers a question about a given paragraph, our technique determines the most relevant frame sequence in the context video to the query video. This is done by representing the individual frames of the two videos by highly discriminative and compact descriptors. Next, the descriptors are fed into a multi-stage network that is able, with the help of the bidirectional attention flow mechanism, to represent the context video at various granularity levels besides estimating the query-aware context part. The proposed model was trained on 10k video-pairs collected from 'YouTube'. The obtained results show that our model outperforms all known state of the art techniques by a considerable margin, confirming its efficacy.

Original languageEnglish
Article number8963636
Pages (from-to)18097-18109
Number of pages13
JournalIEEE Access
Publication statusPublished - 2020


  • Bi-directional attention
  • temporal alignment
  • video alignment
  • video retrieval
  • video synchronization

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering
  • Electrical and Electronic Engineering


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