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New paper on ``Learning Disentangled Representations of Video with Missing Data '', To appear at #NeurIPS2020.
Paper:https://papers.nips.cc/paper/2020/file/24f2f931f12a4d9149876a5bef93e96a-Paper.pdf …
Code:https://github.com/Rose-STL-Lab/DIVE …
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A five-month-old child can understand that objects continue to exist even when they are unseen, a phenomenon known as “object permanence."
However, current #DeepLearning methods cannot reason about the objects when they are missing in #videos.
(2/4)pic.twitter.com/G8IerxFOIm
We present a deep generative model that imputes and predicts future video frames in the presence of missing data. Our model significantly improves the robustness of video prediction methods against missing data, and thereby increasing their practical values. (3/4)pic.twitter.com/mEEcnVnmzw
This work came out of my graduate-level class, led by amazing Ph.D. student Armand Comas @Northeastern!
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Using this technique would it be possible to upscale videos to higher frame-rates? Do things like colour and shadow impact the deep learning method? Or could this technique be further generalised?
Excellent questions! Yes, scaling to higher frame rates is easier for this technique. Shadow impacts the model more. Happy to discuss generalizations :)
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