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Neural OmniVideo: Fusing World Knowledge into Smart Video-Specific Models

CORDIS · observation · Publication date unknown

Neural OmniVideo: Fusing World Knowledge into Smart Video-Specific Models The field of computer vision has made unprecedented progress in applying Deep Learning (DL) to images. Nevertheless, expanding this progress to videos is dramatically lagging behind, due to two key challenges: (i) video data is highly complex and diverse, requiring order of magnitude more training data than images, and (ii) raw video data is extremely high dimensional. These challenges make the processing of entire video pixel-volumes at scale prohibitively expensive and ineffective. Thus, applying DL at scale to video is restricted to short clips or aggressively sub-sampled videos. On the other side of the spectrum, video-specific models—a single or a few neural networks trained on a single video—exhibit several key properties: (i) facilitate effective video representations (e.g., layers) that make video analysis and editing significantly more tractable, (ii) enable long-range temporal analysis by encoding the video through the network, and (iii) are not re

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recordType

award

status

SIGNED

region

EU

value

1500000

unit

EUR