提出动态时序槽变压器，提升手术视频无监督目标发现精度。
Future Slot Prediction for Unsupervised Object Discovery in Surgical Video
- 设计动态时序槽变压器，自适应预测未来最佳槽初始化。
- 在多个手术数据库上达当前最优，显著提升槽解析准确性。
- 适合医疗视觉分析、实时手术理解等临床场景应用。
以对象为中心的槽注意力是新兴的无监督学习范式，可生成结构化、可解释的对象中心表示（槽），实现低计算成本下的有效对象与事件推理，适用于实时手术视频解析等关键医疗应用。然而，真实场景如手术中场景异质性强，难以解析为有意义的槽集合。现有自适应槽数量方法在图像上表现良好，但在手术视频上效果不佳。为此，我们提出动态时序槽变压器（DTST）模块，同时训练其进行时序推理和预测最优未来槽初始化。该模型在多个手术数据库上达到当前最优性能，证明无监督对象中心方法可应用于真实世界数据，并有望成为医疗应用中的常规工具。
原文摘要 · Abstract (English)
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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