3 citations · 5 across the 5 of their papers we have counts for
14 papers · 1 filter
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…