no code implementations • 11 Feb 2022 • Arnaud Huaulmé, Kanako Harada, Quang-Minh Nguyen, Bogyu Park, Seungbum Hong, Min-Kook Choi, Michael Peven, Yunshuang Li, Yonghao Long, Qi Dou, Satyadwyoom Kumar, Seenivasan Lalithkumar, Ren Hongliang, Hiroki Matsuzaki, Yuto Ishikawa, Yuriko Harai, Satoshi Kondo, Mamoru Mitsuishi, Pierre Jannin
The improvement between video/kinematic-based methods and the uni-modality ones was significant for all of the teams.
Ranked #1 on Semantic Segmentation on PETRAW
Scene context is a powerful constraint on the geometry of objects within the scene in cases, such as surveillance, where the camera geometry is unknown and image quality may be poor.
There are many realistic applications of activity recognition where the set of potential activity descriptions is combinatorially large.
In this work, we introduce a novel representation of motion as a voxelized 3D vector field and demonstrate how it can be used to improve performance of action recognition networks.
Our method results in a mean absolute error of 0. 814 N in the ex vivo study, suggesting that it may be a promising alternative to hardware based surgical force feedback in endoscopic procedures.