no code implementations • 14 Jul 2022 • Xia Yuan, Jianping Gou, Baosheng Yu, Jiali Yu, Zhang Yi
Specifically, we design the intra-class compactness constraint on the intermediate representation at different levels to encourage the intra-class representations to be closer to each other, and eventually the learned representation becomes more discriminative.~Unlike the traditional DDL methods, during the classification stage, our DDLIC performs a layer-wise greedy optimization in a similar way to the training stage.
no code implementations • 27 Jul 2021 • Jie Li, Sheng Zhang, Kai Han, Xia Yuan, Chunxia Zhao, Yu Liu
UGV-KPNet is computationally efficient with a small number of parameters and provides pixel-level accurate keypoints detection results in real-time.
1 code implementation • CVPR 2020 • Jie Li, Kai Han, Peng Wang, Yu Liu, Xia Yuan
In contrast to the standard 3D convolution that is limited to a fixed 3D receptive field, our module is capable of modeling the dimensional anisotropy voxel-wisely.
no code implementations • 17 Feb 2020 • Yu Liu, Jie Li, Qingsen Yan, Xia Yuan, Chunxia Zhao, Ian Reid, Cesar Cadena
This paper tackles the problem of data fusion in the semantic scene completion (SSC) task, which can simultaneously deal with semantic labeling and scene completion.
Ranked #14 on 3D Semantic Scene Completion on NYUv2
1 code implementation • 29 Jan 2020 • Yu Liu, Jie Li, Xia Yuan, Chunxia Zhao, Roland Siegwart, Ian Reid, Cesar Cadena
We propose PALNet, a novel hybrid network for SSC based on single depth.
no code implementations • CVPR 2019 • Jie Li, Yu Liu, Dong Gong, Qinfeng Shi, Xia Yuan, Chunxia Zhao, Ian Reid
RGB images differentiate from depth images as they carry more details about the color and texture information, which can be utilized as a vital complementary to depth for boosting the performance of 3D semantic scene completion (SSC).
Ranked #19 on 3D Semantic Scene Completion on NYUv2
no code implementations • 24 Jan 2019 • Xia Yuan, Liao xiaoli, Li Shilei, Shi Qinwen, Wu Jinfa, Li Ke
The results show that the proposed multitask SVM classification model based on 1-2gram TF-IDF features exhibits the best performance among the tested models.