no code implementations • 5 Jan 2024 • Ryo Fujii, Ryo Hachiuma, Hideo Saito
We further propose a co-occurrence loss, which considers a characteristic that some tool pairs often co-occur together in an image to leverage image-level labels.
no code implementations • 11 May 2023 • Aneeq Zia, Kiran Bhattacharyya, Xi Liu, Max Berniker, Ziheng Wang, Rogerio Nespolo, Satoshi Kondo, Satoshi Kasai, Kousuke Hirasawa, Bo Liu, David Austin, Yiheng Wang, Michal Futrega, Jean-Francois Puget, Zhenqiang Li, Yoichi Sato, Ryo Fujii, Ryo Hachiuma, Mana Masuda, Hideo Saito, An Wang, Mengya Xu, Mobarakol Islam, Long Bai, Winnie Pang, Hongliang Ren, Chinedu Nwoye, Luca Sestini, Nicolas Padoy, Maximilian Nielsen, Samuel Schüttler, Thilo Sentker, Hümeyra Husseini, Ivo Baltruschat, Rüdiger Schmitz, René Werner, Aleksandr Matsun, Mugariya Farooq, Numan Saaed, Jose Renato Restom Viera, Mohammad Yaqub, Neil Getty, Fangfang Xia, Zixuan Zhao, Xiaotian Duan, Xing Yao, Ange Lou, Hao Yang, Jintong Han, Jack Noble, Jie Ying Wu, Tamer Abdulbaki Alshirbaji, Nour Aldeen Jalal, Herag Arabian, Ning Ding, Knut Moeller, Weiliang Chen, Quan He, Muhammad Bilal, Taofeek Akinosho, Adnan Qayyum, Massimo Caputo, Hunaid Vohra, Michael Loizou, Anuoluwapo Ajayi, Ilhem Berrou, Faatihah Niyi-Odumosu, Lena Maier-Hein, Danail Stoyanov, Stefanie Speidel, Anthony Jarc
Unfortunately, obtaining the annotations needed to train machine learning models to identify and localize surgical tools is a difficult task.
1 code implementation • 7 Apr 2023 • Mana Masuda, Ryo Hachiuma, Ryo Fujii, Hideo Saito, Yusuke Sekikawa
We propose a deep variational autoencoder-based unsupervised anomaly detection network adapted to the 3D point cloud and an anomaly score specifically for 3D point clouds.
no code implementations • 28 Mar 2023 • Ryo Hachiuma, Tomohiro Shimizu, Hideo Saito, Hiroki Kajita, Yoshifumi Takatsume
Recording surgery in operating rooms is an essential task for education and evaluation of medical treatment.
no code implementations • CVPR 2023 • Fumiaki Sato, Ryo Hachiuma, Taiki Sekii
Particularly, during the training phase using normal samples, the method models the distribution of skeleton features of the normal actions while freezing the weights of the DNNs and estimates the anomaly score using this distribution in the inference phase.
no code implementations • CVPR 2023 • Ryo Hachiuma, Fumiaki Sato, Taiki Sekii
A point cloud deep-learning paradigm is introduced to the action recognition, and a unified framework along with a novel deep neural network architecture called Structured Keypoint Pooling is proposed.
Ranked #1 on Skeleton Based Action Recognition on HMDB51
no code implementations • 14 Mar 2022 • Ryo Fujii, Jayakorn Vongkulbhisal, Ryo Hachiuma, Hideo Saito
However, most works rely on a key assumption that each video is successfully preprocessed by detection and tracking algorithms and the complete observed trajectory is always available.
no code implementations • 14 Oct 2021 • Ryo Fujii, Ryo Hachiuma, Hideo Saito
We expand conventional image inpainting method to RGB-D image inpainting to jointly restore the texture and geometry of missing regions from a pair of RGB and depth images.
no code implementations • 27 Jul 2021 • Alessio Xompero, Santiago Donaher, Vladimir Iashin, Francesca Palermo, Gökhan Solak, Claudio Coppola, Reina Ishikawa, Yuichi Nagao, Ryo Hachiuma, Qi Liu, Fan Feng, Chuanlin Lan, Rosa H. M. Chan, Guilherme Christmann, Jyun-Ting Song, Gonuguntla Neeharika, Chinnakotla Krishna Teja Reddy, Dinesh Jain, Bakhtawar Ur Rehman, Andrea Cavallaro
In this paper, we present a range of methods and an open framework to benchmark acoustic and visual perception for the estimation of the capacity of a container, and the type, mass, and amount of its content.
1 code implementation • NeurIPS 2021 • Zhengyi Luo, Ryo Hachiuma, Ye Yuan, Kris Kitani
By comparing the pose instructed by the kinematic model against the pose generated by the dynamics model, we can use their misalignment to further improve the kinematic model.
Egocentric Pose Estimation Human-Object Interaction Detection +2
no code implementations • 10 Nov 2020 • Zhengyi Luo, Ryo Hachiuma, Ye Yuan, Shun Iwase, Kris M. Kitani
We propose a method for incorporating object interaction and human body dynamics into the task of 3D ego-pose estimation using a head-mounted camera.
no code implementations • 7 Oct 2020 • Moi Hoon Yap, Ryo Hachiuma, Azadeh Alavi, Raphael Brungel, Bill Cassidy, Manu Goyal, Hongtao Zhu, Johannes Ruckert, Moshe Olshansky, Xiao Huang, Hideo Saito, Saeed Hassanpour, Christoph M. Friedrich, David Ascher, Anping Song, Hiroki Kajita, David Gillespie, Neil D. Reeves, Joseph Pappachan, Claire O'Shea, Eibe Frank
DFUC2020 provided participants with a comprehensive dataset consisting of 2, 000 images for training and 2, 000 images for testing.
no code implementations • 22 Jul 2019 • Ryo Hachiuma, Christian Pirchheim, Dieter Schmalstieg, Hideo Saito
We present DetectFusion, an RGB-D SLAM system that runs in real-time and can robustly handle semantically known and unknown objects that can move dynamically in the scene.