9 citations · 22 across the 14 of their papers we have counts for
15 papers
VIOLA: Towards Video In-Context Learning with Minimal Annotations
Ryo Fujii, Hideo Saito, Ryo Hachiuma
Generalizing Multimodal Large Language Models (MLLMs) to novel video domains is essential for real-world deployment but remains challenging due to the scarcity of labeled data. Whi…
Human Preference-Aligned Concept Customization Benchmark via Decomposed Evaluation
Reina Ishikawa, Ryo Fujii, Hideo Saito +1
Evaluating concept customization is challenging, as it requires a comprehensive assessment of fidelity to generative prompts and concept images. Moreover, evaluating multiple conce…
Towards Predicting Any Human Trajectory In Context
Ryo Fujii, Hideo Saito, Ryo Hachiuma
Predicting accurate future trajectories of pedestrians is essential for autonomous systems but remains a challenging task due to the need for adaptability in different environments…
RealTraj: Towards Real-World Pedestrian Trajectory Forecasting
Ryo Fujii, Hideo Saito, Ryo Hachiuma
This paper jointly addresses three key limitations in conventional pedestrian trajectory forecasting: pedestrian perception errors, real-world data collection costs, and person ID…
CrowdMAC: Masked Crowd Density Completion for Robust Crowd Density Forecasting
Ryo Fujii, Ryo Hachiuma, Hideo Saito
A crowd density forecasting task aims to predict how the crowd density map will change in the future from observed past crowd density maps. However, the past crowd density maps are…
EMAG: Ego-motion Aware and Generalizable 2D Hand Forecasting from Egocentric Videos
Masashi Hatano, Ryo Hachiuma, Hideo Saito
Predicting future human behavior from egocentric videos is a challenging but critical task for human intention understanding. Existing methods for forecasting 2D hand positions rel…
Multimodal Cross-Domain Few-Shot Learning for Egocentric Action Recognition
Masashi Hatano, Ryo Hachiuma, Ryo Fujii +1
We address a novel cross-domain few-shot learning task (CD-FSL) with multimodal input and unlabeled target data for egocentric action recognition. This paper simultaneously tackles…
Weakly Semi-supervised Tool Detection in Minimally Invasive Surgery Videos
Ryo Fujii, Ryo Hachiuma, Hideo Saito
Surgical tool detection is essential for analyzing and evaluating minimally invasive surgery videos. Current approaches are mostly based on supervised methods that require large, f…
Intuitive Surgical SurgToolLoc and SurgVU Challenges Results: 2022-2025
Aneeq Zia, Max Berniker, Rogerio Garcia Nespolo +153
Robotic assisted (RA) surgery promises to transform surgical intervention. Intuitive Surgical is committed to fostering these changes and the machine learning models and algorithms…
Toward Unsupervised 3D Point Cloud Anomaly Detection using Variational Autoencoder
Mana Masuda, Ryo Hachiuma, Ryo Fujii +2
In this paper, we present an end-to-end unsupervised anomaly detection framework for 3D point clouds. To the best of our knowledge, this is the first work to tackle the anomaly det…
Deep Selection: A Fully Supervised Camera Selection Network for Surgery Recordings
Ryo Hachiuma, Tomohiro Shimizu, Hideo Saito +2
Recording surgery in operating rooms is an essential task for education and evaluation of medical treatment. However, recording the desired targets, such as the surgery field, surg…
A Two-Block RNN-based Trajectory Prediction from Incomplete Trajectory
Ryo Fujii, Jayakorn Vongkulbhisal, Ryo Hachiuma +1
Trajectory prediction has gained great attention and significant progress has been made in recent years. However, most works rely on a key assumption that each video is successfull…
RGB-D Image Inpainting Using Generative Adversarial Network with a Late Fusion Approach
Ryo Fujii, Ryo Hachiuma, Hideo Saito
Diminished reality is a technology that aims to remove objects from video images and fills in the missing region with plausible pixels. Most conventional methods utilize the differ…
Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation
Moi Hoon Yap, Ryo Hachiuma, Azadeh Alavi +18
There has been a substantial amount of research involving computer methods and technology for the detection and recognition of diabetic foot ulcers (DFUs), but there is a lack of s…
DetectFusion: Detecting and Segmenting Both Known and Unknown Dynamic Objects in Real-time SLAM
Ryo Hachiuma, Christian Pirchheim, Dieter Schmalstieg +1
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. Our syst…