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Graph Convolutional Networks for Multi-modality Movie Scene Segmentation
Yaoxin Li · Alexander Wong · Mohammad Javad Shafiee
Event URL: https://eventhosts.gather.town/app/kR7ip0Bhhn8BXuMD/wiml-workshop-2021 »

A typical movie scene is comprised of a number of different shots, edited together to form a narrative thread. The intricate transitions between different shots within a movie scene allow filmmakers to tell the story or convey a message in a clear and vivid manner. As a result of the complexity in the interactions between individuals and their actions within a movie scene, a major challenge in movie semantic understanding is that of scene segmentation, where the goal is to identify the individual scenes within a movie. A key part of the challenge with scene segmentation is the fact that a movie scene may be comprised of multiple uncut shots filmed over an uninterrupted period of time, leading to a visually discontinuous yet semantically coherent segment. Therefore, while separating a movie into individual shots can be accomplished based on visual continuity between frames, separating a movie into individual scenes requires a much deeper understanding of the semantics of a film and the relationship between shots that are semantically consistent but physically distinct.

Author Information

Yaoxin Li (University of Waterloo)
Alexander Wong (University of Waterloo)
Mohammad Javad Shafiee (University of Waterloo)

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