no code implementations • 13 Nov 2023 • Ruiquan Ge, Xiangyang Hu, Rungen Huang, Gangyong Jia, Yaqi Wang, Renshu Gu, Changmiao Wang, Elazab Ahmed, Linyan Wang, Juan Ye, Ye Li
In TTMFN, we present a two-stream multimodal co-attention transformer module to take full advantage of the complex relationships between different modalities and the potential connections within the modalities.
no code implementations • 25 May 2022 • Yuxing Chen, Renshu Gu, Ouhan Huang, Gangyong Jia
The proposed VTP framework integrates the high performance of the transformer with volumetric representations, which can be used as a good alternative to the convolutional backbones.
Ranked #4 on 3D Human Pose Estimation on Panoptic (using extra training data)
no code implementations • 19 Nov 2021 • Zhizheng Jiang, Fei Gao, Renshu Gu, Jinlan Xu, Gang Xu, Timon Rabczuk
In this paper, a novel deep learning framework is proposed for temporal super-resolution simulation of blood vessel flows, in which a high-temporal-resolution time-varying blood vessel flow simulation is generated from a low-temporal-resolution flow simulation result.
1 code implementation • ICCV 2021 • Gaoang Wang, Renshu Gu, Zuozhu Liu, Weijie Hu, Mingli Song, Jenq-Neng Hwang
In this paper, we try to explore the significance of motion patterns for vehicle tracking without appearance information.
1 code implementation • 1 Aug 2021 • Kaibing Yang, Renshu Gu, Maoyu Wang, Masahiro Toyoura, Gang Xu
The lack of diverse and accurate pose and shape training data becomes a major bottleneck, especially for scenes with occlusions in the wild.
Ranked #2 on 3D Human Shape Estimation on SSP-3D
no code implementations • 6 May 2021 • Gaoang Wang, Yizhou Wang, Renshu Gu, Weijie Hu, Jenq-Neng Hwang
To address such common challenges in most of the existing trackers, in this paper, a tracklet booster algorithm is proposed, which can be built upon any other tracker.
no code implementations • 31 Oct 2020 • Renshu Gu, Gaoang Wang, Jenq-Neng Hwang
Videos that contain multiple potentially occluded people captured from freely moving monocular cameras are very common in real-world scenarios, while 3D HPE for such scenarios is quite challenging, partially because there is a lack of such data with accurate 3D ground truth labels in existing datasets.
1 code implementation • 18 Nov 2018 • Gaoang Wang, Yizhou Wang, Haotian Zhang, Renshu Gu, Jenq-Neng Hwang
Multi-object tracking (MOT) is an important and practical task related to both surveillance systems and moving camera applications, such as autonomous driving and robotic vision.
Ranked #19 on Multi-Object Tracking on MOT16