Sierra Bonilla, Fengyi Jiang, Chinedu Nwoye, Jingpei Lu, Kailey Reardon, Humphrey W. Chow, Francisco Vasconcelos, Sophia Bano 等 10 位作者
We introduce iMED, the first synchronized multi-endoscope dataset for robot-assisted minimally invasive surgery (RAMIS), designed to address a fundamental limitation in surgical vision benchmarking: the absence of independent held-out viewpoints. Existing surgical datasets capture single-trajectory sequences from a single endoscope, making it impossible to distinguish geometric generalization from photometric interpolation along a narrow forward-facing path. iMED provides 340 sequences ~170K synchronized timepoints with 4 views per timepoint) recorded simultaneously from two independent stereo endoscopes across ex vivo, postmortem, and live surgical settings, spanning 14 specimens with diverse anatomical regions and motion regimes. The dataset includes calibrated camera intrinsics, ArUco-based frame-wise pose estimates with uncertainty quantification, instrument segmentation masks, and rich clinical metadata. Using a train-on-one-endoscope, test-on-another evaluation protocol, we benchmark 23 state-of-the-art methods across rigid and deformable novel view synthesis, pose estimation, feature matching, and monocular depth estimation. Our experiments show that methods relying primarily on photometric supervision degrade substantially under this held-out-endoscope setting, while methods with explicit geometric regularization are more robust across views in our benchmark. Notably, large-scale foundation models transfer surprisingly well to surgical imagery, often outperforming domain-specific models, suggesting pitfalls in current fine-tuning protocols. iMED establishes a new evaluation protocol for geometric generalization in surgical vision.
Stefanos Gkikas, Thomas Kassiotis, Yu Fang, Christian Arzate Cruz, Miao Cheng, Zongyi Jiang, Zacharias Kamarianakis, Houshyar Asadi 等 11 位作者
Stress is a leading contributor to long-term physical and mental health decline. The automatic assessment of stress is a promising approach for continuous monitoring and mitigating human biases. However, the development of automated systems remains challenging due to the heterogeneity of physiological and behavioral signals. This paper presents MUPA${}^2$S, a multimodal framework for automatic stress assessment that combines facial video and ECG signals within a unified perception architecture. Both modalities are mapped into a shared token representation without modality-specific components, and processed through asymmetric attention comprising segment-local cross-attention and global self-attention across segment states. Experiments on a newly collected 58-subject stress dataset, evaluated under a stratified subject-level protocol, show that facial video achieves 69.44% test accuracy, while ECG-only performance remains close to chance under the proposed representation. Unified Video-ECG fusion achieves a test accuracy of 69.58% at approximately half the number of parameters of feature-level fusion, with negligible additional computational cost. Feature-level fusion does not exceed the video-only baseline, while unified input-level integration provides a parameter-efficient mechanism for jointly processing the two modalities.