no code implementations • 21 Apr 2024 • Xiaoran Zhao, Tianhao Wu, Yu Lai, Zhiliang Tian, Zhen Huang, Yahui Liu, Zejiang He, Dongsheng Li
Controllable text-to-image generation synthesizes visual text and objects in images with certain conditions, which are frequently applied to emoji and poster generation.
no code implementations • 22 Mar 2024 • Jiawen Kang, Xiaofeng Luo, Jiangtian Nie, Tianhao Wu, Haibo Zhou, Yonghua Wang, Dusit Niyato, Shiwen Mao, Shengli Xie
As highly computerized avatars of Vehicular Metaverse Users (VMUs), the Vehicle Twins (VTs) deployed in edge servers can provide valuable metaverse services to improve driving safety and on-board satisfaction for their VMUs throughout journeys.
no code implementations • 21 Mar 2024 • Tianhao Wu, Chuanxia Zheng, Tat-Jen Cham, Qianyi Wu
3D decomposition/segmentation still remains a challenge as large-scale 3D annotated data is not readily available.
no code implementations • 29 Dec 2023 • Qian Wang, Tianhao Wu
Hamilton-Jacobi Reachability Analysis is a formal verification method that guarantees performance and safety for dynamical systems and is widely applicable to various tasks and challenges.
no code implementations • 23 Nov 2023 • Ling Feng, Danyang Li, Tianhao Wu, Xuliang Duan
Specifically, it is proposed to take fragmented student models divided from the complete student model as lower-grade models.
no code implementations • 30 Sep 2023 • Tianhao Wu, Banghua Zhu, Ruoyu Zhang, Zhaojin Wen, Kannan Ramchandran, Jiantao Jiao
In summary, this work introduces a simpler yet effective approach for aligning LLMs to human preferences through relative feedback.
no code implementations • 12 Sep 2023 • Tianhao Wu, Mingdong Wu, Jiyao Zhang, Yunchong Gan, Hao Dong
In this paper, we propose a novel task called human-assisting dexterous grasping that aims to train a policy for controlling a robotic hand's fingers to assist users in grasping objects.
no code implementations • 23 Aug 2023 • Wenzhao Li, Tianhao Wu, Fangcheng Zhong, Cengiz Oztireli
We highlight a research gap in radiance fields style transfer, the lack of sufficient perceptual controllability, motivated by the existing concept in the 2D image style transfer.
no code implementations • 29 Jul 2023 • Jiawen Kang, Jinbo Wen, Dongdong Ye, Bingkun Lai, Tianhao Wu, Zehui Xiong, Jiangtian Nie, Dusit Niyato, Yang Zhang, Shengli Xie
Given the revolutionary role of metaverses, healthcare metaverses are emerging as a transformative force, creating intelligent healthcare systems that offer immersive and personalized services.
no code implementations • 6 Jul 2023 • Tianhao Wu, Chuanxia Zheng, Tat-Jen Cham
Generating complete 360-degree panoramas from narrow field of view images is ongoing research as omnidirectional RGB data is not readily available.
no code implementations • 24 Mar 2023 • Hanxue Liang, Tianhao Wu, Param Hanji, Francesco Banterle, Hongyun Gao, Rafal Mantiuk, Cengiz Oztireli
We measured the quality of videos synthesized by several NVS methods in a well-controlled perceptual quality assessment experiment as well as with many existing state-of-the-art image/video quality metrics.
no code implementations • 17 Mar 2023 • Tianhao Wu, Hanxue Liang, Fangcheng Zhong, Gernot Riegler, Shimon Vainer, Cengiz Oztireli
While neural radiance field (NeRF) based methods can model semi-transparency and achieve photo-realistic quality in synthesized novel views, their volumetric geometry representation tightly couples geometry and opacity, and therefore cannot be easily converted into surfaces without introducing artifacts.
no code implementations • NeurIPS 2023 • Yunchang Yang, Han Zhong, Tianhao Wu, Bin Liu, LiWei Wang, Simon S. Du
We study stochastic delayed feedback in general multi-agent sequential decision making, which includes bandits, single-agent Markov decision processes (MDPs), and Markov games (MGs).
1 code implementation • 28 Sep 2022 • Tianhao Wu, Boyang Li, Yihang Luo, Yingqian Wang, Chao Xiao, Ting Liu, Jungang Yang, Wei An, Yulan Guo
Due to the extremely large image coverage area (e. g., thousands square kilometers), candidate targets in these images are much smaller, dimer, more changeable than those targets observed by aerial-based and land-based imaging devices.
1 code implementation • 7 Aug 2022 • Qiyu Dai, Jiyao Zhang, Qiwei Li, Tianhao Wu, Hao Dong, Ziyuan Liu, Ping Tan, He Wang
Commercial depth sensors usually generate noisy and missing depths, especially on specular and transparent objects, which poses critical issues to downstream depth or point cloud-based tasks.
1 code implementation • 17 Jun 2022 • Yuanpei Chen, Tianhao Wu, Shengjie Wang, Xidong Feng, Jiechuang Jiang, Stephen Marcus McAleer, Yiran Geng, Hao Dong, Zongqing Lu, Song-Chun Zhu, Yaodong Yang
In this study, we propose the Bimanual Dexterous Hands Benchmark (Bi-DexHands), a simulator that involves two dexterous hands with tens of bimanual manipulation tasks and thousands of target objects.
no code implementations • 31 May 2022 • Tianhao Wu, Fangcheng Zhong, Andrea Tagliasacchi, Forrester Cole, Cengiz Oztireli
We introduce Decoupled Dynamic Neural Radiance Field (D$^2$NeRF), a self-supervised approach that takes a monocular video and learns a 3D scene representation which decouples moving objects, including their shadows, from the static background.
1 code implementation • CVPR 2022 • Klaus Greff, Francois Belletti, Lucas Beyer, Carl Doersch, Yilun Du, Daniel Duckworth, David J. Fleet, Dan Gnanapragasam, Florian Golemo, Charles Herrmann, Thomas Kipf, Abhijit Kundu, Dmitry Lagun, Issam Laradji, Hsueh-Ti, Liu, Henning Meyer, Yishu Miao, Derek Nowrouzezahrai, Cengiz Oztireli, Etienne Pot, Noha Radwan, Daniel Rebain, Sara Sabour, Mehdi S. M. Sajjadi, Matan Sela, Vincent Sitzmann, Austin Stone, Deqing Sun, Suhani Vora, Ziyu Wang, Tianhao Wu, Kwang Moo Yi, Fangcheng Zhong, Andrea Tagliasacchi
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and training details.
no code implementations • 4 Mar 2022 • Tianhao Wu, Fangwei Zhong, Yiran Geng, Hongchen Wang, Yongjian Zhu, Yizhou Wang, Hao Dong
we formulate the dynamic grasping problem as a 'move-and-grasp' game, where the robot is to pick up the object on the mover and the adversarial mover is to find a path to escape it.
1 code implementation • 1 Mar 2022 • Zheng Yuan, Tianhao Wu, Qinwen Wang, Yiying Yang, Lei LI, Lin Zhang
Although there are some achievements in the field of MVP in the open space environment, the urban area brings complicated road structures and restricted moving spaces as challenges to the resolution of MVP games.
no code implementations • 21 Dec 2021 • Tianhao Wu, Yunchang Yang, Han Zhong, LiWei Wang, Simon S. Du, Jiantao Jiao
Policy optimization methods are one of the most widely used classes of Reinforcement Learning (RL) algorithms.
no code implementations • 19 Dec 2021 • Mingxin Yu, Lin Shao, Zhehuan Chen, Tianhao Wu, Qingnan Fan, Kaichun Mo, Hao Dong
Part assembly is a typical but challenging task in robotics, where robots assemble a set of individual parts into a complete shape.
1 code implementation • 9 Aug 2021 • Zhe Wang, Xinhang Li, Tianhao Wu, Chen Xu, Lin Zhang
This paper proposes a Swarm-Federated Deep Learning framework in the IoV system (IoV-SFDL) that integrates SL into the FDL framework.
no code implementations • ICLR 2022 • Ruihai Wu, Yan Zhao, Kaichun Mo, Zizheng Guo, Yian Wang, Tianhao Wu, Qingnan Fan, Xuelin Chen, Leonidas Guibas, Hao Dong
In this paper, we propose object-centric actionable visual priors as a novel perception-interaction handshaking point that the perception system outputs more actionable guidance than kinematic structure estimation, by predicting dense geometry-aware, interaction-aware, and task-aware visual action affordance and trajectory proposals.
no code implementations • ICLR 2022 • Yunchang Yang, Tianhao Wu, Han Zhong, Evrard Garcelon, Matteo Pirotta, Alessandro Lazaric, LiWei Wang, Simon S. Du
We also obtain a new upper bound for conservative low-rank MDP.
no code implementations • 1 Dec 2020 • Mingzhi Jiang, Tianhao Wu, Zhe Wang, Yi Gong, Lin Zhang, Ren Ping Liu
In particular, we propose a Multi-intersection Vehicular Cooperative Control (MiVeCC) to enable cooperation among vehicles in a large area with multiple unsignalized intersections.
1 code implementation • NeurIPS 2020 • Jingtong Su, Yihang Chen, Tianle Cai, Tianhao Wu, Ruiqi Gao, Li-Wei Wang, Jason D. Lee
In this paper, we conduct sanity checks for the above beliefs on several recent unstructured pruning methods and surprisingly find that: (1) A set of methods which aims to find good subnetworks of the randomly-initialized network (which we call "initial tickets"), hardly exploits any information from the training data; (2) For the pruned networks obtained by these methods, randomly changing the preserved weights in each layer, while keeping the total number of preserved weights unchanged per layer, does not affect the final performance.
1 code implementation • 7 Jul 2020 • Yingqian Wang, Jungang Yang, Longguang Wang, Xinyi Ying, Tianhao Wu, Wei An, Yulan Guo
In this paper, we propose a deformable convolution network (i. e., LF-DFnet) to handle the disparity problem for LF image SR.
1 code implementation • 10 Dec 2019 • Yingqian Wang, Tianhao Wu, Jungang Yang, Longguang Wang, Wei An, Yulan Guo
In this paper, we handle the LF de-occlusion (LF-DeOcc) problem using a deep encoder-decoder network (namely, DeOccNet).