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_attentionimport 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)To use the adaptive slot method for generating a differentiable one hot mask for whether to use a slot, just do the following
import torch
from slot_attention import MultiHeadSlotAttention, AdaptiveSlotWrapper
# define slot attention
slot_attn = MultiHeadSlotAttention(
dim = 512,
num_slots = 5,
iters = 3,
)
# wrap the slot attention
adaptive_slots = AdaptiveSlotWrapper(
slot_attn,
temperature = 0.5 # gumbel softmax temperature
)
inputs = torch.randn(2, 1024, 512)
slots, keep_slots = adaptive_slots(inputs) # (2, 5, 512), (2, 5)
# the auxiliary loss in the paper for minimizing number of slots used for a scene would simply be
keep_aux_loss = keep_slots.sum() # add this to your main loss with some weight@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}
}@article{Fan2024AdaptiveSA,
title = {Adaptive Slot Attention: Object Discovery with Dynamic Slot Number},
author = {Ke Fan and Zechen Bai and Tianjun Xiao and Tong He and Max Horn and Yanwei Fu and Francesco Locatello and Zheng Zhang},
journal = {ArXiv},
year = {2024},
volume = {abs/2406.09196},
url = {https://api.semanticscholar.org/CorpusID:270440447}
}
