slot_attention 1.5.2

Implementation of Slot Attention in Pytorch

Slot Attention

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

Install

$ pip install slot_attention

Usage

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)

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

Citations

@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}
}

@article{liu2025metaslot,
    title   = {MetaSlot: Break Through the Fixed Number of Slots in Object-Centric Learning},
    author  = {Liu, Hongjia and Zhao, Rongzhen and Chen, Haohan and Pajarinen, Joni},
    journal = {Advances in Neural Information Processing Systems},
    volume  = {38},
    pages   = {67319--67344},
    year    = {2026}
}

@inproceedings{Touska2026OrthoRF,
    title   = {OrthoRF: Exploring Orthogonality in Object-Centric Representations},
    author  = {Despoina Touska and Bastiaan Onne Fagginger Auer and Alexandru Onose and Tejaswi Kasarla and Luis Armando P{\'e}rez Rey and Maximilian Lipp and Lyubov Amitonova and Martin R. Oswald and Pascal Cerfontaine},
    booktitle = {International Conference on Learning Representations},
    year    = {2026}
}