Future Slot Prediction for Unsupervised Object Discovery in Surgical Video
Journal:
arXiv
Published Date:
Jul 2, 2025
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.
