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First Online: 18 September 2023

Ethical Statement

: Our work aims to improve the object representations of object discovery models, specifically targeting the improvements of their use in additional modules in downstream reasoning tasks. With the improvements of our training scheme, it is feasible to integrate the findings of unsupervised object discovery methods into practical use-cases. A main motivation, as stated in our introduction, is that such an integration of high-quality object-centric representations is beneficial for more human-centric AI. Arguably, it seems beneficial for humans to perceive, communicate and explain the world on the level of objects. Integrating such level of abstraction and representation to AI agents is a necessary step for fruitful and reliable human-AI interactions.Obviously, our work is not unaffected from the dual-use dilemma of foundational (AI) research. And a watchful eye should be kept, particularly on object detection research which can easily be misused, <i>e.g.</i> for involuntary human surveillance. However, our work or implications thereof do not, to the best of our knowledge, pose an obvious direct threat to any individuals or society in general.

Conference Information

Conference Acronym: ECML PKDD

Conference Name: Joint European Conference on Machine Learning and Knowledge Discovery in Databases

Conference City: Turin

Conference Country: Italy

Conference Year: 2023

Conference Start Date: 18 September 2023

Conference End Date: 22 September 2023

Conference Number: 23

Conference ID: ecml2023

Conference URL: https://2023.ecmlpkdd.org/

Peer Review Information (provided by the conference organizers)

Type: Double-blind

Conference Management System: CMT

Number of Submissions Sent for Review: 829

Number of Full Papers Accepted: 196

Number of Short Papers Accepted: 0

Acceptance Rate of Full Papers: 24% - The value is computed by the equation "Number of Full Papers Accepted / Number of Submissions Sent for Review * 100" and then rounded to a whole number.

Average Number of Reviews per Paper: 3.63

Average Number of Papers per Reviewer: 4.5

External Reviewers Involved: Yes

Additional Info on Review Process: Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted papers.