no code implementations • 13 May 2023 • Luke Friedman, Sameer Ahuja, David Allen, Zhenning Tan, Hakim Sidahmed, Changbo Long, Jun Xie, Gabriel Schubiner, Ajay Patel, Harsh Lara, Brian Chu, Zexi Chen, Manoj Tiwari
A Conversational Recommender System (CRS) offers increased transparency and control to users by enabling them to engage with the system through a real-time multi-turn dialogue.
no code implementations • 12 Jun 2022 • Zexi Chen, Yiyi Liao, Haozhe Du, Haodong Zhang, Xuecheng Xu, Haojian Lu, Rong Xiong, Yue Wang
Next, the rotation, scale, and translation are independently and efficiently estimated in the spectrum step-by-step using the DPC solver.
no code implementations • 7 Mar 2022 • Xianze Fang, Yunkai Wang, Zexi Chen, Yue Wang, Rong Xiong
The depth completion task aims to complete a per-pixel dense depth map from a sparse depth map.
no code implementations • 25 Sep 2021 • Zexi Chen, Haozhe Du, Xuecheng Xu, Rong Xiong, Yiyi Liao, Yue Wang
Specifically, we first adopt Unscented Kalman Filter as a differentiable layer to predict the pitch and roll, where the covariance matrices of noise are learned to filter out the noise of the IMU raw data.
1 code implementation • 22 Sep 2021 • Yunkai Wang, Dongkun Zhang, Yuxiang Cui, Zexi Chen, Wei Jing, Junbo Chen, Rong Xiong, Yue Wang
In this paper, we propose a domain generalization method for vision-based driving trajectory generation for autonomous vehicles in urban environments, which can be seen as a solution to extend the Invariant Risk Minimization (IRM) method in complex problems.
1 code implementation • 7 Mar 2021 • Zexi Chen, Zheyuan Huang, Yunkai Wang, Xuecheng Xu, Yue Wang, Rong Xiong
In this paper, we propose the network SSDS that learns a way of distinguishing small defections between two images regardless of the context, so that the network can be trained once for all.
no code implementations • 1 Mar 2021 • Yunshuang Li, Zheyuan Huang, Zexi Chen, Yue Wang, Rong Xiong
Taking the aerial robots' advantages of having large scale variance of view points of the same route which the ground robots is on, the collaboration work provides global information of road segmentation for the ground robot, thus enabling it to obtain feasible region and adjust its pose ahead of time.
1 code implementation • 31 Oct 2020 • Zexi Chen, Jiaxin Guo, Xuecheng Xu, Yunkai Wang, Yue Wang, Rong Xiong
Utilizing the trained model under different conditions without data annotation is attractive for robot applications.
1 code implementation • 21 Oct 2020 • Xuecheng Xu, Huan Yin, Zexi Chen, Yue Wang, Rong Xiong
In this paper, we propose a LiDAR-based place recognition method, named Differentiable Scan Context with Orientation (DiSCO), which simultaneously finds the scan at a similar place and estimates their relative orientation.
2 code implementations • 20 Oct 2020 • Yunkai Wang, Dongkun Zhang, Jingke Wang, Zexi Chen, Yue Wang, Rong Xiong
One of the challenges to reduce the gap between the machine and the human level driving is how to endow the system with the learning capacity to deal with the coupled complexity of environments, intentions, and dynamics.
Robotics
2 code implementations • 21 Aug 2020 • Zexi Chen, Xuecheng Xu, Yue Wang, Rong Xiong
The crucial step for localization is to match the current observation to the map.
no code implementations • 8 Jul 2020 • Zexi Chen, Bharathkumar Ramachandra, Ranga Raju Vatsavai
Our experiments show that this new composite consistency regularization based semi-GAN significantly improves its performance and achieves new state-of-the-art performance among GAN-based SSL approaches.
1 code implementation • 20 Apr 2020 • Zexi Chen, Benjamin Dutton, Bharathkumar Ramachandra, Tianfu Wu, Ranga Raju Vatsavai
In MT, each data point is considered independent of other points during training; however, data points are likely to be close to each other in feature space if they share similar features.
1 code implementation • 22 May 2019 • Zheyuan Huang, Lingyun Chen, Jiacheng Li, Yunkai Wang, Zexi Chen, Licheng Wen, Jianyang Gu, Peng Hu, Rong Xiong
For the Small Size League of RoboCup 2018, Team ZJUNLict has won the champion and therefore, this paper thoroughly described the devotion which ZJUNLict has devoted and the effort that ZJUNLict has contributed.
Robotics 68T40
no code implementations • 16 Nov 2018 • Zexi Chen, Bharathkumar Ramachandra, Tianfu Wu, Ranga Raju Vatsavai
By doing this, our Relational LSTM is capable of capturing long and short-range spatio-temporal relations between objects in videos in a principled way.