For a frame in video 1, TCC finds the nearest neighbor (NN) in video 2. To go back to video 1, we find the nearest neighbor of NN in video 1. If we came back to the frame we started from, the frames are cycle-consistent. TCC minimizes this cycle-consistency error.pic.twitter.com/9dUqwI4Ao0
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ML highlights from the paper: 1. Cycle-consistency loss applied directly on low dimensional embeddings (without GAN / decoder). 2. Soft-nearest neighbors to find correspondences across videos. Training method:pic.twitter.com/GnD6jw9ZSX
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TCC discovers the phases of an action without additional labels. In this video, we retrieve nearest neighbors in the embedding space to frames in the reference video. In spite of many variations, TCC maps semantically similar frames to nearby points in the embedding space.pic.twitter.com/k4o4y4o6gE
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Self-supervised methods are quite useful in the few-shot setting. Consider the action phase classification task. With only 1 labeled video TCC achieves similar performance to vanilla supervised learning models trained with ~50 videos.pic.twitter.com/Xu26Tpr68y
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Some applications of the per-frame embeddings learned using TCC: 1. Unsupervised video alignmentpic.twitter.com/bAMpiOIRwd
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This is joint work with
@yusufaytar , Jonathan Tompson,@psermanet and Andrew Zisserman.Prikaži ovu nit
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Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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Hello, the Penn Action dataset referred to in your paper does not have the key events and phase labels you mentioned after downloading. Can you disclose your work in order to reproduce your work?
@debidatta Thank youHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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