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arXiv · 2507.01882

Future Slot Prediction for Unsupervised Object Discovery in Surgical Video

Guiqiu Liao·Matjaz Jogan·Marcel Hussing·Edward Zhang·Eric Eaton·Daniel A. Hashimoto
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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.

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Guiqiu Liao, Matjaz Jogan, Marcel Hussing, Edward Zhang, Eric Eaton, Daniel A. Hashimoto. 2025-07-02. Future Slot Prediction for Unsupervised Object Discovery in Surgical Video. https://arxiv.org/abs/2507.01882

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