Search Results for author: Jinming Cao

Found 5 papers, 4 papers with code

ShapeMoiré: Channel-Wise Shape-Guided Network for Image Demoiréing

1 code implementation28 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.

SOGDet: Semantic-Occupancy Guided Multi-view 3D Object Detection

1 code implementation26 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.

3D Object Detection Autonomous Driving +2

ShapeConv: Shape-aware Convolutional Layer for Indoor RGB-D Semantic Segmentation

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.

Segmentation Semantic Segmentation +1

DO-Conv: Depthwise Over-parameterized Convolutional Layer

1 code implementation22 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.

Image Classification

DiDA: Disentangled Synthesis for Domain Adaptation

no code implementations21 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.

Disentanglement Unsupervised Domain Adaptation

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