1 code implementation • 28 Mar 2024 • Yunpeng Zhang, Deheng Qian, Ding Li, Yifeng Pan, Yong Chen, Zhenbao Liang, Zhiyao Zhang, Shurui Zhang, Hongxu Li, Maolei Fu, Yun Ye, Zhujin Liang, Yi Shan, Dalong Du
With the representation of the ISG, the driving agents aggregate essential information from the most influential elements, including the road agents with potential collisions and the map elements to follow.
1 code implementation • 28 Feb 2024 • Chaokang Jiang, Guangming Wang, Jiuming Liu, Hesheng Wang, Zhuang Ma, Zhenqiang Liu, Zhujin Liang, Yi Shan, Dalong Du
We present a novel approach from the perspective of auto-labelling, aiming to generate a large number of 3D scene flow pseudo labels for real-world LiDAR point clouds.
1 code implementation • 13 Nov 2023 • JunJie Huang, Yun Ye, Zhujin Liang, Yi Shan, Dalong Du
3D object Detection with LiDAR-camera encounters overfitting in algorithm development which is derived from the violation of some fundamental rules.
1 code implementation • 24 Mar 2023 • Bohan Li, Yasheng Sun, Zhujin Liang, Dalong Du, Zhuanghui Zhang, XiaoFeng Wang, Yunnan Wang, Xin Jin, Wenjun Zeng
However, due to the inherent representation gap between stereo geometry and BEV features, it is non-trivial to bridge them for dense prediction task of SSC.
no code implementations • 13 Jul 2015 • Zhujin Liang, Shengyong Ding, Liang Lin
This paper investigates how to rapidly and accurately localize facial landmarks in unconstrained, cluttered environments rather than in the well segmented face images.
no code implementations • NeurIPS 2014 • Xiaolong Wang, Liliang Zhang, Liang Lin, Zhujin Liang, WangMeng Zuo
We present a general joint task learning framework, in which each task (either object localization or object segmentation) is tackled via a multi-layer convolutional neural network, and the two networks work collaboratively to boost performance.
no code implementations • 2 Feb 2015 • Zhujin Liang, Xiaolong Wang, Rui Huang, Liang Lin
This paper aims at one newly raising task in vision and multimedia research: recognizing human actions from still images.