Excited to share our work on Conditional Object-Centric Learning from Video!

We introduce SAVi, a slot-based model that can discover + represent visual entities in videos, using simple location cues and object motion (...or entirely unsupervised)

🖥️ slot-attention-video.github.io

1/7 When trained entirely unsupervised (by simply reconstructing the input video), SAVi learns to decompose videos into meaningful entities, such as objects or parts that move independently.

While this works on (simple) real data, such as in this robotic grasping environment...

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Excited to share our work @GoogleAI on Object-centric Learning with Slot Attention!

Slot Attention is a simple module for structure discovery and set prediction: it uses iterative attention to group perceptual inputs into a set of slots.

Paper: arxiv.org/abs/2006.15055

[1/7] Slot Attention is related to self-attention, with some crucial differences that effectively turn it into a meta-learned clustering algorithm.

Slots are randomly initialized for each example and then iteratively refined. Everything is symmetric under permutation.

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