---
OA_place: repository
OA_type: green
_id: '18964'
abstract:
- lang: eng
  text: Object-centric learning (OCL) extracts the representation of objects with
    slots, offering an exceptional blend of flexibility and interpretability for abstracting
    low-level perceptual features. A widely adopted method within OCL is slot attention,
    which utilizes attention mechanisms to iteratively refine slot representations.
    However, a major draw-back of most object-centric models, including slot attention,
    is their reliance on predefining the number of slots. This not only necessitates
    prior knowledge of the dataset but also overlooks the inherent variability in
    the number of objects present in each instance. To overcome this fundamental limitation,
    we present a novel complexity-aware object auto-encoder framework. Within this
    framework, we introduce an adaptive slot attention (AdaSlot) mecha-nism that dynamically
    determines the optimal number of slots based on the content of the data. This
    is achieved by proposing a discrete slot sampling module that is responsible for
    selecting an appropriate number of slots from a candidate list. Furthermore, we
    introduce a masked slot decoder that suppresses unselected slots during the decoding
    process. Our framework, tested extensively on object discovery tasks with various
    datasets, shows performance matching or exceeding top fixed-slot models. Moreover,
    our analysis substantiates that our method exhibits the capability to dynamically
    adapt the slot number according to each instance's complexity, offering the potential
    for further exploration in slot attention research. Project will be available
    at https://kfan21.github.io/AdaSlot/
acknowledgement: Yanwei Fu is the corresponding authour. Yanwei Fu is with School
  of Data Science, Fudan University, Shanghai Key Lab of Intelligent Information Processing,
  Fudan University, and Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence,
  Zhejiang Normal University, Jinhua, China.
article_processing_charge: No
arxiv: 1
author:
- first_name: Ke
  full_name: Fan, Ke
  last_name: Fan
- first_name: Zechen
  full_name: Bai, Zechen
  last_name: Bai
- first_name: Tianjun
  full_name: Xiao, Tianjun
  last_name: Xiao
- first_name: Tong
  full_name: He, Tong
  last_name: He
- first_name: Max
  full_name: Horn, Max
  last_name: Horn
- first_name: Yanwei
  full_name: Fu, Yanwei
  last_name: Fu
- first_name: Francesco
  full_name: Locatello, Francesco
  id: 26cfd52f-2483-11ee-8040-88983bcc06d4
  last_name: Locatello
  orcid: 0000-0002-4850-0683
- first_name: Zheng
  full_name: Zhang, Zheng
  last_name: Zhang
citation:
  ama: 'Fan K, Bai Z, Xiao T, et al. Adaptive slot attention: Object discovery with
    dynamic slot number. In: <i>2024 IEEE/CVF Conference on Computer Vision and Pattern
    Recognition</i>. IEEE; 2024. doi:<a href="https://doi.org/10.1109/cvpr52733.2024.02176">10.1109/cvpr52733.2024.02176</a>'
  apa: 'Fan, K., Bai, Z., Xiao, T., He, T., Horn, M., Fu, Y., … Zhang, Z. (2024).
    Adaptive slot attention: Object discovery with dynamic slot number. In <i>2024
    IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>. Seattle, WA,
    United States: IEEE. <a href="https://doi.org/10.1109/cvpr52733.2024.02176">https://doi.org/10.1109/cvpr52733.2024.02176</a>'
  chicago: 'Fan, Ke, Zechen Bai, Tianjun Xiao, Tong He, Max Horn, Yanwei Fu, Francesco
    Locatello, and Zheng Zhang. “Adaptive Slot Attention: Object Discovery with Dynamic
    Slot Number.” In <i>2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>.
    IEEE, 2024. <a href="https://doi.org/10.1109/cvpr52733.2024.02176">https://doi.org/10.1109/cvpr52733.2024.02176</a>.'
  ieee: 'K. Fan <i>et al.</i>, “Adaptive slot attention: Object discovery with dynamic
    slot number,” in <i>2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>,
    Seattle, WA, United States, 2024.'
  ista: 'Fan K, Bai Z, Xiao T, He T, Horn M, Fu Y, Locatello F, Zhang Z. 2024. Adaptive
    slot attention: Object discovery with dynamic slot number. 2024 IEEE/CVF Conference
    on Computer Vision and Pattern Recognition. CVPR: Conference on Computer Vision
    and Pattern Recognition.'
  mla: 'Fan, Ke, et al. “Adaptive Slot Attention: Object Discovery with Dynamic Slot
    Number.” <i>2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>,
    IEEE, 2024, doi:<a href="https://doi.org/10.1109/cvpr52733.2024.02176">10.1109/cvpr52733.2024.02176</a>.'
  short: K. Fan, Z. Bai, T. Xiao, T. He, M. Horn, Y. Fu, F. Locatello, Z. Zhang, in:,
    2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2024.
conference:
  end_date: 2024-06-22
  location: Seattle, WA, United States
  name: 'CVPR: Conference on Computer Vision and Pattern Recognition'
  start_date: 2024-06-16
date_created: 2025-01-29T14:27:39Z
date_published: 2024-06-15T00:00:00Z
date_updated: 2025-09-09T12:15:17Z
day: '15'
department:
- _id: FrLo
doi: 10.1109/cvpr52733.2024.02176
external_id:
  arxiv:
  - '2406.09196'
  isi:
  - '001342515506043'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://doi.org/10.48550/arXiv.2406.09196
month: '06'
oa: 1
oa_version: Preprint
publication: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition
publication_identifier:
  eisbn:
  - '9798350353006'
publication_status: published
publisher: IEEE
quality_controlled: '1'
related_material:
  link:
  - relation: software
    url: https://kfan21.github.io/AdaSlot/
status: public
title: 'Adaptive slot attention: Object discovery with dynamic slot number'
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...