1 citations · 2 across the 12 of their papers we have counts for
13 papers
AnyGroundBench: A Multi-Domain Adaptation Benchmark for Video Grounding in VLMs
Rintaro Otsubo, Ryo Fujii, Reina Ishikawa +6
Vision-Language Models (VLMs) have shown strong performance in Spatio-Temporal Video Grounding (STVG), yet they are still evaluated mostly in a zero-shot manner on general-purpose…
Learning from Synthetic Data via Provenance-Based Input Gradient Guidance
Koshiro Nagano, Ryo Fujii, Ryo Hachiuma +3
Learning methods using synthetic data have attracted attention as an effective approach for increasing the diversity of training data while reducing collection costs, thereby impro…
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…
EgoSurgery-HTS: A Dataset for Egocentric Hand-Tool Segmentation in Open Surgery Videos
Nathan Darjana, Ryo Fujii, Hideo Saito +1
Egocentric open-surgery videos capture rich, fine-grained details essential for accurately modeling surgical procedures and human behavior in the operating room. A detailed, pixel-…
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…
EgoSurgery-Tool: A Dataset of Surgical Tool and Hand Detection from Egocentric Open Surgery Videos
Ryo Fujii, Hideo Saito, Hiroki Kajita
Surgical tool detection is a fundamental task for understanding egocentric open surgery videos. However, detecting surgical tools presents significant challenges due to their highl…
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…
EgoSurgery-Phase: A Dataset of Surgical Phase Recognition from Egocentric Open Surgery Videos
Ryo Fujii, Masashi Hatano, Hideo Saito +1
Surgical phase recognition has gained significant attention due to its potential to offer solutions to numerous demands of the modern operating room. However, most existing methods…
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…