1 code implementation • 28 Apr 2024 • Jinming Cao, Sicheng Shen, Qiu Zhou, Yifang Yin, Yangyan Li, Roger Zimmermann
Interestingly, we find that the Shape information effectively captures the moir\'e patterns in artifact images.
1 code implementation • 26 Aug 2023 • Qiu Zhou, Jinming Cao, Hanchao Leng, Yifang Yin, Yu Kun, Roger Zimmermann
This indicates that the combination of 3D object detection and 3D semantic occupancy leads to a more comprehensive perception of the 3D environment, thereby aiding build more robust autonomous driving systems.
1 code implementation • ICCV 2021 • Jinming Cao, Hanchao Leng, Dani Lischinski, Danny Cohen-Or, Changhe Tu, Yangyan Li
The reason is that the learnt weights for balancing the importance between the shape and base components in ShapeConv become constants in the inference phase, and thus can be fused into the following convolution, resulting in a network that is identical to one with vanilla convolutional layers.
Ranked #3 on Semantic Segmentation on Stanford2D3D - RGBD
1 code implementation • 22 Jun 2020 • Jinming Cao, Yangyan Li, Mingchao Sun, Ying Chen, Dani Lischinski, Daniel Cohen-Or, Baoquan Chen, Changhe Tu
Moreover, in the inference phase, the depthwise convolution is folded into the conventional convolution, reducing the computation to be exactly equivalent to that of a convolutional layer without over-parameterization.
no code implementations • 21 May 2018 • Jinming Cao, Oren Katzir, Peng Jiang, Dani Lischinski, Danny Cohen-Or, Changhe Tu, Yangyan Li
The key idea is that by learning to separately extract both the common and the domain-specific features, one can synthesize more target domain data with supervision, thereby boosting the domain adaptation performance.