no code implementations • 5 Jun 2024 • Haoran Cheng, Liang Peng, Linxuan Xia, Yuepeng Hu, Hengjia Li, Qinglin Lu, Xiaofei He, Boxi Wu
Specifically, we divide T2V generation process into two steps: (i) For a given prompt input, we search existing text-video datasets to find videos with text labels that closely match the prompt motions.
no code implementations • 30 Apr 2024 • Zhanwei Zhang, Minghao Chen, Shuai Xiao, Liang Peng, Hengjia Li, Binbin Lin, Ping Li, Wenxiao Wang, Boxi Wu, Deng Cai
Specifically, in the selection process, to improve the reliability of pseudo boxes, we propose a complementary augmentation strategy.
1 code implementation • 18 Mar 2024 • Yang Yang, Wen Wang, Liang Peng, Chaotian Song, Yao Chen, Hengjia Li, Xiaolong Yang, Qinglin Lu, Deng Cai, Boxi Wu, Wei Liu
Customization generation techniques have significantly advanced the synthesis of specific concepts across varied contexts.
1 code implementation • 6 Mar 2024 • Liang Peng, Junyuan Gao, Xinran Liu, Weihong Li, Shaohua Dong, Zhipeng Zhang, Heng Fan, Libo Zhang
The rich annotations of VastTrack enables development of both the vision-only and the vision-language tracking.
no code implementations • 22 Dec 2023 • Liang Peng, Songyue Cai, Zongqian Wu, Huifang Shang, Xiaofeng Zhu, Xiaoxiao Li
Nonetheless, applying existing prompt learning methods for the diagnosis of neurological disorder still suffers from two issues: (i) existing methods typically treat all patches equally, despite the fact that only a small number of patches in neuroimaging are relevant to the disease, and (ii) they ignore the structural information inherent in the brain connection network which is crucial for understanding and diagnosing neurological disorders.
1 code implementation • 19 Dec 2023 • Junkai Xu, Liang Peng, Haoran Cheng, Linxuan Xia, Qi Zhou, Dan Deng, Wei Qian, Wenxiao Wang, Deng Cai
To resolve this problem, we propose to regulate intermediate dense 3D features with the help of volume rendering.
no code implementations • 14 Dec 2023 • Yibo Zhao, Liang Peng, Yang Yang, Zekai Luo, Hengjia Li, Yao Chen, Wei Zhao, Qinglin Lu, Boxi Wu, Wei Liu
In this paper, we introduce a new simple yet practical task setting: local control.
1 code implementation • 29 Nov 2023 • Liang Peng, Haoran Cheng, Zheng Yang, Ruisi Zhao, Linxuan Xia, Chaotian Song, Qinglin Lu, Boxi Wu, Wei Liu
By applying the loss to existing one-shot video tuning methods, we significantly improve the overall consistency and smoothness of the generated videos.
1 code implementation • ICCV 2023 • Junkai Xu, Liang Peng, Haoran Cheng, Hao Li, Wei Qian, Ke Li, Wenxiao Wang, Deng Cai
To the best of our knowledge, this work is the first to introduce volume rendering for M3D, and demonstrates the potential of implicit reconstruction for image-based 3D perception.
1 code implementation • 3 Aug 2023 • Liang Peng, Xin Wang, Xiaofeng Zhu
Unsupervised multiplex graph learning (UMGL) has been shown to achieve significant effectiveness for different downstream tasks by exploring both complementary information and consistent information among multiple graphs.
1 code implementation • 27 Jul 2023 • Lingdong Kong, Yaru Niu, Shaoyuan Xie, Hanjiang Hu, Lai Xing Ng, Benoit R. Cottereau, Ding Zhao, Liangjun Zhang, Hesheng Wang, Wei Tsang Ooi, Ruijie Zhu, Ziyang Song, Li Liu, Tianzhu Zhang, Jun Yu, Mohan Jing, Pengwei Li, Xiaohua Qi, Cheng Jin, Yingfeng Chen, Jie Hou, Jie Zhang, Zhen Kan, Qiang Ling, Liang Peng, Minglei Li, Di Xu, Changpeng Yang, Yuanqi Yao, Gang Wu, Jian Kuai, Xianming Liu, Junjun Jiang, Jiamian Huang, Baojun Li, Jiale Chen, Shuang Zhang, Sun Ao, Zhenyu Li, Runze Chen, Haiyong Luo, Fang Zhao, Jingze Yu
In this paper, we summarize the winning solutions from the RoboDepth Challenge -- an academic competition designed to facilitate and advance robust OoD depth estimation.
no code implementations • 28 Jun 2023 • Chuanyue Shen, Letian Zhang, Zhangsihao Yang, Masood Mortazavi, Xiyun Song, Liang Peng, Heather Yu
Efficient photorealistic rendering of human 3D dynamics is the core of immersive meetings.
1 code implementation • 25 May 2023 • Liang Peng, Junkai Xu, Haoran Cheng, Zheng Yang, Xiaopei Wu, Wei Qian, Wenxiao Wang, Boxi Wu, Deng Cai
Monocular 3D detection is a challenging task due to the lack of accurate 3D information.
no code implementations • 11 Jan 2023 • Wenbo Shao, Yanchao Xu, Liang Peng, Jun Li, Hong Wang
Motion prediction is essential for safe and efficient autonomous driving.
no code implementations • 14 Nov 2022 • Xiaopei Wu, Yang Zhao, Liang Peng, Hua Chen, Xiaoshui Huang, Binbin Lin, Haifeng Liu, Deng Cai, Wanli Ouyang
When training a teacher-student semi-supervised framework, we randomly select gt samples and pseudo samples to both labeled frames and unlabeled frames, making a strong data augmentation for them.
1 code implementation • 8 Nov 2022 • Liang Peng, Boqi Li, Wenhao Yu, Kai Yang, Wenbo Shao, Hong Wang
Therefore, this paper proposes the "Self-Surveillance and Self-Adaption System" as a systematic approach to online minimize the SOTIF risk, which aims to provide a systematic solution for monitoring, quantification, and mitigation of inherent and external risks.
1 code implementation • 7 Nov 2022 • Liang Peng, Jun Li, Wenbo Shao, Hong Wang
Perception algorithms in autonomous driving systems confront great challenges in long-tail traffic scenarios, where the problems of Safety of the Intended Functionality (SOTIF) could be triggered by the algorithm performance insufficiencies and dynamic operational environment.
1 code implementation • 18 Jul 2022 • Liang Peng, Xiaopei Wu, Zheng Yang, Haifeng Liu, Deng Cai
Therefore, we propose to reformulate the instance depth to the combination of the instance visual surface depth (visual depth) and the instance attribute depth (attribute depth).
1 code implementation • CVPR 2022 • Xiaopei Wu, Liang Peng, Honghui Yang, Liang Xie, Chenxi Huang, Chengqi Deng, Haifeng Liu, Deng Cai
Many multi-modal methods are proposed to alleviate this issue, while different representations of images and point clouds make it difficult to fuse them, resulting in suboptimal performance.
1 code implementation • 17 Mar 2022 • Liang Peng, Nan Wang, Jie Xu, Xiaofeng Zhu, Xiaoxiao Li
To improve fMRI representation learning and classification under a label-efficient setting, we propose a novel and theory-driven self-supervised learning (SSL) framework on GCNs, namely Graph CCA for Temporal self-supervised learning on fMRI analysis GATE.
1 code implementation • ICLR 2022 • Liang Peng, Senbo Yan, Boxi Wu, Zheng Yang, Xiaofei He, Deng Cai
This network is learned by minimizing our newly-proposed 3D alignment loss between the 3D box estimates and the corresponding RoI LiDAR points.
no code implementations • 19 Dec 2021 • Liang Peng, Nan Wang, Nicha Dvornek, Xiaofeng Zhu, Xiaoxiao Li
Then we train a global GCN node classifier across institutions using a federated graph learning platform.
no code implementations • 29 Sep 2021 • Liang Peng, Senbo Yan, Chenxi Huang, Xiaofei He, Deng Cai
This characteristic indicates that monocular 3D detection is inherently different from other typical detection tasks that have the same dimensional input and output.
1 code implementation • CVPR 2022 • Jie Xu, Huayi Tang, Yazhou Ren, Liang Peng, Xiaofeng Zhu, Lifang He
Our method learns different levels of features from the raw features, including low-level features, high-level features, and semantic labels/features in a fusion-free manner, so that it can effectively achieve the reconstruction objective and the consistency objectives in different feature spaces.
no code implementations • 28 Apr 2021 • Chen-Chen Fan, Haiqun Xie, Liang Peng, Hongjun Yang, Zhen-Liang Ni, Guan'an Wang, Yan-Jie Zhou, Sheng Chen, Zhijie Fang, Shuyun Huang, Zeng-Guang Hou
On the DMS data set, GF-DANN has obtained an accuracy rate of 89. 47%, and the sensitivity and specificity are 90% and 89%.
1 code implementation • 19 Apr 2021 • Liang Peng, Fei Liu, Zhengxu Yu, Senbo Yan, Dan Deng, Zheng Yang, Haifeng Liu, Deng Cai
We delve into this underlying mechanism and then empirically find that: concerning the label accuracy, the 3D location part in the label is preferred compared to other parts of labels.
no code implementations • 13 Apr 2021 • Liang Peng, Fei Liu, Senbo Yan, Xiaofei He, Deng Cai
Image-only and pseudo-LiDAR representations are commonly used for monocular 3D object detection.
no code implementations • 21 Oct 2020 • Liang Peng, Dan Deng, Deng Cai
Occlusion handling is a challenging problem in stereo matching, especially for unsupervised methods.