Discover and read the best of Twitter Threads about #supervised
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The curation week is almost over and I would like to thank everyone for joining the discussions this week! It’s been a blast 🥳
If you enjoyed this week, feel free to reach out on Twitter (@cosima_meyer) or GitHub (github.com/cosimameyer/) ✨
If you enjoyed this week, feel free to reach out on Twitter (@cosima_meyer) or GitHub (github.com/cosimameyer/) ✨
@cosima_meyer I feel very honored that I had the chance to talk with you about the things I enjoy doing and I cannot wait to learn more from the upcoming curators - the lineup looks amazing! 💜
@cosima_meyer If you missed a Twitter thread this week, head over to @pilizalde's amazing thread where she collected all of them (I love the GitHub emoji 😺)
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ACM #Multimedia 2021: Skeleton-Contrastive 3D Action Representation Learning w/ @fmthoker, @doughty_hazel: arxiv.org/abs/2108.03656 We learn invariances to multiple #skeleton representations and introduce various skeleton augmentations via noise contrastive estimation 1/n 
Contribution I: leverage multiple input-representations of #3D-#skeleton sequences. Our inter-skeleton contrast learns from a pair of representations in a cross-contrastive fashion. Enriches the sparse input space and focuses on the high-level semantics of the skeleton data. 2/n
Contribution II: three skeleton-specific #augmentations for generating positive pairs which encourage the model to focus on the spatio- temporal #dynamics of skeleton-based @action sequences, ignoring confounding factors such as viewpoint and exact joint positions. 3/n
