Sounding Surgical Instrument Localisation through Audio-Visual Correspondence
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Spatial localization of surgical instruments through bounding-box annotations is a foundational component of surgical scene understanding, supporting detection, tracking, interaction analysis, and gro...
arXiv:2610.34218v1 Announce Type: new Abstract: Spatial localization of surgical instruments through bounding-box annotations is a foundational component of surgical scene understanding, supporting d...
arXiv:2609.23067v1 Announce Type: new Abstract: Referring surgical video instrument segmentation (RSVIS) aims at segmenting the instrument in a surgical video, given a textual description. Despite re...
Effective multi-task learning for surgical scene understanding is fundamentally hindered by annotation granularity mismatch; temporal workflow tasks such as phase recognition, step recognition and anticipation benefit from dense frame-level supervision, whereas pixel-level spatial tasks including instrument segmentation and action recognition are only sparsely annotated on selected keyframes due to prohibitive labeling costs. This supervision imbalance undermines shared representation learning and limits joint optimization across heterogeneous surgical tasks.
arXiv:2608.02471v2 Announce Type: replace-cross Abstract: In laparoscopy, surgeon gaze tracks where the instruments will act; easing this demand through visual attention modeling requires dense label...
The paper introduces a semantic RGB–Depth framework for assessing microsurgical skill from microscopic stereo videos. It fuses sparse metric stereo depth with dense monocular depth to create a dense geometric scene representation, which is combined with semantically segmented RGB streams of instruments and anatomy. Using a hierarchical attention architecture, the model achieves an F1 score of 0.938 for skill-level classification, outperforming RGB-only and depth-only baselines.