Surgical robotics holds much promise for improving patient safety and
clinician experience in the Operating Room (OR). However, it also comes with
new challenges, requiring strong team coordination and effective OR management.
Automatic detection of surgical activities is a key requirement for developing
AI-based intelligent tools to tackle these challenges. The current
state-of-the-art surgical activity recognition methods however operate on
image-based representations and depend on large-scale labeled datasets whose
collection is time-consuming and resource-expensive. This work proposes a new
sample-efficient and object-based approach for surgical activity recognition in
the OR. Our method focuses on the geometric arrangements between clinicians and
surgical devices, thus utilizing the significant object interaction dynamics in
the OR. We conduct experiments in a low-data regime study for long video
activity recognition. We also benchmark our method againstother object-centric
approaches on clip-level action classification and show superior performance.

手术机器人在手术室中产生了很大的潜力，但也带来了新的挑战，需要强大的团队协调和有效的手术室管理。本文提出了一种新的样本有效和基于对象的手术活动识别方法，着重利用手术室中临床医生和手术设备之间的几何排列以及对象间的交互动力学。通过在低数据条件下进行长视频活动识别的实验，并与其他基于对象的方法在剪辑级别的动作分类上进行测试，展现了出色的性能。

ST (OR) 2：手术室活动识别的时空对象级推理

ST(OR)2: Spatio-Temporal Object Level Reasoning for Activity Recognition  in the Operating Room

Indoor relocalization is vital for both robotic tasks like autonomous
exploration and civil applications such as navigation with a cell phone in a
shopping mall. Some previous approaches adopt geometrical information such as
key-point features or local textures to carry out indoor relocalization, but
they either easily fail in an environment with visually similar scenes or
require many database images. Inspired by the fact that humans often remember
places by recognizing unique landmarks, we resort to objects, which are more
informative than geometry elements. In this work, we propose a simple yet
effective object-based indoor relocalization approach, dubbed AirLoc. To
overcome the critical challenges of object reidentification and remembering
object relationships, we extract object-wise appearance embedding and
inter-object geometric relationships. The geometry and appearance features are
integrated to generate cumulative scene features. This results in a robust,
accurate, and portable indoor relocalization system, which outperforms the
state-of-the-art methods in room-level relocalization by 9.5% of PR-AUC and 7%
of accuracy. In addition to exhaustive evaluation, we also carry out real-world
tests, where AirLoc shows robustness in challenges like severe occlusion,
perceptual aliasing, viewpoint shift, and deformation.

本文提出了一种简单而有效的基于物体的室内重定位方法，称为 AirLoc。通过提取物体外观嵌入和物体间的几何关系来解决物体重新识别和记忆物体关系的关键挑战，并将几何和外观特征相结合生成累积场景特征，从而实现了强大、准确、便携的室内重定位系统，并在室内重定位的 PR-AUC 和准确性方面优于现有方法 9.5% 和 7%。