no code implementations • 19 Dec 2023 • Haolin Liu, Chongjie Ye, Yinyu Nie, Yingfan He, Xiaoguang Han
Instance shape reconstruction from a 3D scene involves recovering the full geometries of multiple objects at the semantic instance level.
no code implementations • 4 Nov 2023 • Haolin Liu, Rajmohan Rajaraman, Ravi Sundaram, Anil Vullikanti, Omer Wasim, Haifeng Xu
In this paper, we initialize the study of sample complexity in opinion formation to solve this problem.
no code implementations • 17 Oct 2023 • Haolin Liu, Chen-Yu Wei, Julian Zimmert
The first algorithm, although computationally inefficient, ensures a regret of $\widetilde{\mathcal{O}}\left(\sqrt{K}\right)$, where $K$ is the number of episodes.
no code implementations • CVPR 2023 • Xianggang Yu, Mutian Xu, Yidan Zhang, Haolin Liu, Chongjie Ye, Yushuang Wu, Zizheng Yan, Chenming Zhu, Zhangyang Xiong, Tianyou Liang, GuanYing Chen, Shuguang Cui, Xiaoguang Han
The birth of ImageNet drives a remarkable trend of "learning from large-scale data" in computer vision.
1 code implementation • 18 Jul 2022 • Haolin Liu, Yujian Zheng, GuanYing Chen, Shuguang Cui, Xiaoguang Han
We present a new framework to reconstruct holistic 3D indoor scenes including both room background and indoor objects from single-view images.
1 code implementation • 17 Mar 2022 • Mutian Xu, Pei Chen, Haolin Liu, Xiaoguang Han
Experiments show that the algorithms trained on TO-Scene indeed work on the realistic test data, and our proposed tabletop-aware learning strategy greatly improves the state-of-the-art results on both 3D semantic segmentation and object detection tasks.
2 code implementations • CVPR 2021 • Haolin Liu, Anran Lin, Xiaoguang Han, Lei Yang, Yizhou Yu, Shuguang Cui
Grounding referring expressions in RGBD image has been an emerging field.