1 code implementation • ECCV 2020 • Guangming Wu, Yinqiang Zheng, Zhiling Guo, Zekun Cai, Xiaodan Shi, Xin Ding, Yifei HUANG, Yimin Guo, Ryosuke Shibasaki
In silicon sensors, the interference between visible and near-infrared (NIR) signals is a crucial problem.
no code implementations • 22 Feb 2024 • Jiawei Wang, Renhe Jiang, Chuang Yang, Zengqing Wu, Makoto Onizuka, Ryosuke Shibasaki, Chuan Xiao
The key technical contribution is a novel LLM agent framework that accounts for individual activity patterns and motivations, including a self-consistency approach to align LLMs with real-world activity data and a retrieval-augmented strategy for interpretable activity generation.
1 code implementation • 25 Sep 2023 • Zekun Cai, Renhe Jiang, Xinyu Yang, Zhaonan Wang, Diansheng Guo, Hiroki Kobayashi, Xuan Song, Ryosuke Shibasaki
Urban time series data forecasting featuring significant contributions to sustainable development is widely studied as an essential task of the smart city.
Ranked #1 on Traffic Prediction on Beijing Traffic
no code implementations • 20 Jul 2023 • Weihang Ran, Wei Yuan, Xiaodan Shi, Zipei Fan, Ryosuke Shibasaki
Building outline extracted from high-resolution aerial images can be used in various application fields such as change detection and disaster assessment.
no code implementations • 9 Jul 2023 • Zhiling Guo, Xiaodan Shi, Haoran Zhang, Dou Huang, Xiaoya Song, Jinyue Yan, Ryosuke Shibasaki
The development of remote sensing and deep learning techniques has enabled building semantic segmentation with high accuracy and efficiency.
no code implementations • 24 Jun 2023 • Zhiling Guo, Yinqiang Zheng, Haoran Zhang, Xiaodan Shi, Zekun Cai, Ryosuke Shibasaki, Jinyue Yan
In recent years, single-frame image super-resolution (SR) has become more realistic by considering the zooming effect and using real-world short- and long-focus image pairs.
no code implementations • 13 Jan 2023 • Hongjun Wang, Zhiwen Zhang, Zipei Fan, Jiyuan Chen, Lingyu Zhang, Ryosuke Shibasaki, Xuan Song
Subsequently, a Multitask Weakly Supervised Learning Framework for Travel Time Estimation (MWSL TTE) has been proposed to infer transition probability between roads segments, and the travel time on road segments and intersection simultaneously.
no code implementations • 2 Jul 2022 • Zhiwen Zhang, Hongjun Wang, Jiyuan Chen, Zipei Fan, Xuan Song, Ryosuke Shibasaki
However, building a model for such a data-driven task requires a large amount of users' travel information, which directly relates to their privacy and thus is less likely to be shared.
no code implementations • 21 Jun 2022 • Zhiwen Zhang, Hongjun Wang, Zipei Fan, Jiyuan Chen, Xuan Song, Ryosuke Shibasaki
In this case, this paper aims to resolve the problem of travel time estimation (TTE) and route recovery in sparse scenarios, which often leads to the uncertain label of travel time and route between continuously sampled GPS points.
no code implementations • 21 Jun 2022 • Zipei Fan, Xiaojie Yang, Wei Yuan, Renhe Jiang, Quanjun Chen, Xuan Song, Ryosuke Shibasaki
In the first stage, to encode the daily variation of human mobility at a metropolitan level, we automatically extract citywide mobility trends as crowd contexts and predict long-term and long-distance movements at a coarse level.
no code implementations • 6 Apr 2022 • Mingxin Zhang, Zipei Fan, Ryosuke Shibasaki, Xuan Song
We also incorporate graph convolutional networks (GCNs) to extract graph-level embeddings, a feature that has been largely overlooked in previous WiFi indoor localization studies.
1 code implementation • 14 Dec 2021 • Zhaonan Wang, Renhe Jiang, Hao Xue, Flora D. Salim, Xuan Song, Ryosuke Shibasaki
As a decisive part in the success of Mobility-as-a-Service (MaaS), spatio-temporal predictive modeling for crowd movements is a challenging task particularly considering scenarios where societal events drive mobility behavior deviated from the normality.
no code implementations • 21 Nov 2021 • Dou Huang, Haoran Zhang, Xuan Song, Ryosuke Shibasaki
In this paper, we propose to use a differentiable projection layer in DNN instead of directly solving time-consuming KKT conditions.
1 code implementation • CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management 2021 • Zhaonan Wang, Renhe Jiang, Zekun Cai, Zipei Fan, Xin Liu, Kyoung-Sook Kim, Xuan Song, Ryosuke Shibasaki
Forecasting incident occurrences (e. g. crime, EMS, traffic accident) is a crucial task for emergency service providers and transportation agencies in performing response time optimization and dynamic fleet management.
no code implementations • 17 Sep 2021 • Jinyu Chen, Haoran Zhang, Xuan Song, Ryosuke Shibasaki
In this study, we propose and open GPS trajectory dataset marked with travel mode and benchmark for the travel mode detection.
3 code implementations • 20 Aug 2021 • Renhe Jiang, Du Yin, Zhaonan Wang, Yizhuo Wang, Jiewen Deng, Hangchen Liu, Zekun Cai, Jinliang Deng, Xuan Song, Ryosuke Shibasaki
Nowadays, with the rapid development of IoT (Internet of Things) and CPS (Cyber-Physical Systems) technologies, big spatiotemporal data are being generated from mobile phones, car navigation systems, and traffic sensors.
no code implementations • 6 Jul 2021 • Yohei Koga, Hiroyuki Miyazaki, Ryosuke Shibasaki
While recent advancement of domain adaptation techniques is significant, most of methods only align a feature extractor and do not adapt a classifier to target domain, which would be a cause of performance degradation.
1 code implementation • IEEE Transactions on Knowledge and Data Engineering 2021 • Renhe Jiang, Zekun Cai, Zhaonan Wang, Chuang Yang, Zipei Fan, Quanjun Chen, Kota Tsubouchi, Xuan Song, Ryosuke Shibasaki
Based on this idea, a series of methods have been proposed to address grid-based prediction for citywide crowd and traffic.
1 code implementation • 2021 IEEE 37th International Conference on Data Engineering (ICDE) 2021 • Zhaonan Wang, Tianqi Xia, Renhe Jiang, Xin Liu, Kyoung-Sook Kim, Xuan Song, Ryosuke Shibasaki
Forecasting regional ambulance demand plays a fundamental part in dynamic fleet allocation and redeployment.
no code implementations • 25 Nov 2020 • Qianwei Cheng, AKM Mahbubur Rahman, Anis Sarker, Abu Bakar Siddik Nayem, Ovi Paul, Amin Ahsan Ali, M Ashraful Amin, Ryosuke Shibasaki, Moinul Zaber
Image encompassing 70% of the urban space was used for training and the remaining 30% was used for testing and validation.
no code implementations • 16 Nov 2019 • Renhe Jiang, Zekun Cai, Zhaonan Wang, Chuang Yang, Zipei Fan, Xuan Song, Kota Tsubouchi, Ryosuke Shibasaki
In this study, we publish a new aggregated human mobility dataset generated from a real-world smartphone application and build a standard benchmark for such kind of video-like urban computing with this new dataset and the existing open datasets.
1 code implementation • 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2019 • Renhe Jiang, Xuan Song, Dou Huang, Xiaoya Song, Tianqi Xia, Zekun Cai, Zhaonan Wang, Kyoung-Sook Kim, Ryosuke Shibasaki
Therefore in this study, we aim to extract the “deep” trend only from the current momentary observations and generate an accurate prediction for the trend in the short future, which is considered to be an effective way to deal with the event situations.
no code implementations • 28 Sep 2018 • Zhiling Guo, Hiroaki Shengoku, Guangming Wu, Qi Chen, Wei Yuan, Xiaodan Shi, Xiaowei Shao, Yongwei Xu, Ryosuke Shibasaki
The results indicate the proposed method can serve as a viable tool for urban planning map semantic segmentation task with high accuracy and efficiency.
no code implementations • 13 Aug 2017 • Quanshi Zhang, Xuan Song, Ryosuke Shibasaki
In this study, we formulate the concept of "mining maximal-size frequent subgraphs" in the challenging domain of visual data (images and videos).
no code implementations • CVPR 2014 • Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, Ryosuke Shibasaki
3D reconstruction from a single image is a classical problem in computer vision.
no code implementations • CVPR 2014 • Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, Ryosuke Shibasaki
Graph matching and graph mining are two typical areas in artificial intelligence.
no code implementations • CVPR 2013 • Quanshi Zhang, Xuan Song, Xiaowei Shao, Ryosuke Shibasaki, Huijing Zhao
We design a graphical model that uses object edges to represent object structures, and this paper aims to incrementally learn this category model from one labeled object and a number of casually captured scenes.