Implementation of Slot Attention from the paper 'Object-Centric Learning with Slot Attention' in Pytorch. Here is a video that describes what this network can do.
Update: The official repository has been released here
$ pip install slot_attention
import torch
from slot_attention import SlotAttention
slot_attn = SlotAttention(
num_slots = 5,
dim = 512,
iters = 3 # iterations of attention, defaults to 3
)
inputs = torch.randn(2, 1024, 512)
slot_attn(inputs) # (2, 5, 512)
After training, the network is reported to be able to generalize to slightly different number of slots (clusters). You can override the number of slots used by the num_slots
keyword in forward.
slot_attn(inputs, num_slots = 8) # (2, 8, 512)
@misc{locatello2020objectcentric,
title = {Object-Centric Learning with Slot Attention},
author = {Francesco Locatello and Dirk Weissenborn and Thomas Unterthiner and Aravindh Mahendran and Georg Heigold and Jakob Uszkoreit and Alexey Dosovitskiy and Thomas Kipf},
year = {2020},
eprint = {2006.15055},
archivePrefix = {arXiv},
primaryClass = {cs.LG}
}