# Future Slot Prediction for Unsupervised Object Discovery in Surgical Video

Source: [Future Slot Prediction for Unsupervised Object Discovery in Surgical Video](<https://www.sota2.com/research/paper/68fa79f01e209131cea80204>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-02T10:45:35.423Z

Authors: Guiqiu Liao, Matjaz Jogan, Marcel Hussing, Edward Zhang, Eric Eaton, Daniel A. Hashimoto

Published: 2025-07-02T16:52:16.000Z

## Abstract

Object-centric slot attention is an emerging paradigm for unsupervised learning of structured, interpretable object-centric representations (slots). This enables effective reasoning about objects and events at a low computational cost and is thus applicable to critical healthcare applications, such as real-time interpretation of surgical video. The heterogeneous scenes in real-world applications like surgery are, however, difficult to parse into a meaningful set of slots. Current approaches with an adaptive slot count perform well on images, but their performance on surgical videos is low. To address this challenge, we propose a dynamic temporal slot transformer (DTST) module that is trained both for temporal reasoning and for predicting the optimal future slot initialization. The model achieves state-of-the-art performance on multiple surgical databases, demonstrating that unsupervised object-centric methods can be applied to real-world data and become part of the common arsenal in healthcare applications.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Unsupervised object-centric learning on Abdominal surgical dataset (test)](<https://www.sota2.com/research/sota/unsupervised-object-centric-learning-on-abdominal-surgical-dataset-test.md>): Unsupervised object-centric learning; Abdominal surgical dataset (test); mBO-V: 50.8.

- [Unsupervised object-centric learning on Thoracic surgical dataset (test)](<https://www.sota2.com/research/sota/unsupervised-object-centric-learning-on-thoracic-surgical-dataset-test.md>): Unsupervised object-centric learning; Thoracic surgical dataset (test); mBO-V: 38.2.

- [Unsupervised object-centric learning on Cholecystectomy surgical dataset (test)](<https://www.sota2.com/research/sota/unsupervised-object-centric-learning-on-cholecystectomy-surgical-dataset-test.md>): Unsupervised object-centric learning; Cholecystectomy surgical dataset (test); mBO-V: 33.2.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2507.01882>)

[PDF](<https://arxiv.org/pdf/2507.01882.pdf>)

[Implementation](<https://github.com/PCASOlab/Xslot>)