To assess the performances of PT, ECG, and BM, cardiac and respiratory signals extracted from the RT cine images were used as the ground truth.
Independently computing embeddings for questions and answers results in late fusion of information related to matching questions to their answers.
ReVEAL4D is validated using data from eight healthy volunteers and two patients and compared with a compressed sensing technique, L1-SENSE.
Challenges in taking the state-space approach are the estimation of the high-dimensional state of a deformable object from raw images, where annotations are very expensive on real data, and finding a dynamics model that is both accurate, generalizable, and efficient to compute.
With the aim of creating virtual cloth deformations more similar to real world clothing, we propose a new computational framework that recasts three dimensional cloth deformation as an RGB image in a two dimensional pattern space.
Every moment counts in action recognition.
Ranked #5 on Action Detection on Multi-THUMOS