Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Huang, Tengteng"'
The future instance prediction from a Bird's Eye View(BEV) perspective is a vital component in autonomous driving, which involves future instance segmentation and instance motion prediction. Existing methods usually rely on a redundant and complex pi
Externí odkaz:
http://arxiv.org/abs/2404.12867
Autor:
Liu, Feng, Huang, Tengteng, Zhang, Qianjing, Yao, Haotian, Zhang, Chi, Wan, Fang, Ye, Qixiang, Zhou, Yanzhao
Multi-view 3D object detection systems often struggle with generating precise predictions due to the challenges in estimating depth from images, increasing redundant and incorrect detections. Our paper presents Ray Denoising, an innovative method tha
Externí odkaz:
http://arxiv.org/abs/2402.03634
Autor:
Wu, Yuhang, Huang, Tengteng, Yao, Haotian, Zhang, Chi, Shao, Yuanjie, Han, Chuchu, Gao, Changxin, Sang, Nong
Recently, many approaches tackle the Unsupervised Domain Adaptive person re-identification (UDA re-ID) problem through pseudo-label-based contrastive learning. During training, a uni-centroid representation is obtained by simply averaging all the ins
Externí odkaz:
http://arxiv.org/abs/2112.11689
Recently, fusing the LiDAR point cloud and camera image to improve the performance and robustness of 3D object detection has received more and more attention, as these two modalities naturally possess strong complementarity. In this paper, we propose
Externí odkaz:
http://arxiv.org/abs/2112.11088
Model smoothing is of central importance for obtaining a reliable teacher model in the student-teacher framework, where the teacher generates surrogate supervision signals to train the student. A popular model smoothing method is the Temporal Moving
Externí odkaz:
http://arxiv.org/abs/2110.01253
Autor:
Huang, Tengteng, Li, Huawei, Chen, Xiaoling, Chen, Daiwen, He, Jun, Yu, Bing, Luo, Yuheng, Zheng, Ping, Chen, Hong, Huang, Zhiqing
Publikováno v:
In Animal Feed Science and Technology March 2024 309
This paper proposes a new generative adversarial network for pose transfer, i.e., transferring the pose of a given person to a target pose. We design a progressive generator which comprises a sequence of transfer blocks. Each block performs an interm
Externí odkaz:
http://arxiv.org/abs/2103.11622
In this paper, we aim at addressing two critical issues in the 3D detection task, including the exploitation of multiple sensors~(namely LiDAR point cloud and camera image), as well as the inconsistency between the localization and classification con
Externí odkaz:
http://arxiv.org/abs/2007.08856
In this paper, we focus on exploring the robustness of the 3D object detection in point clouds, which has been rarely discussed in existing approaches. We observe two crucial phenomena: 1) the detection accuracy of the hard objects, e.g., Pedestrians
Externí odkaz:
http://arxiv.org/abs/1912.05163
The non-local module works as a particularly useful technique for semantic segmentation while criticized for its prohibitive computation and GPU memory occupation. In this paper, we present Asymmetric Non-local Neural Network to semantic segmentation
Externí odkaz:
http://arxiv.org/abs/1908.07678