activity
20182024
most citedDO-Conv: Depthwise Over-parameterized Convolutional Layer

40 citations · 41 across the 2 of their papers we have counts for

collaborators
Showing cs.CVShow all

5 papers · 1 filter

cs.CV2024

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

Jinming Cao, Sicheng Shen, Qiu Zhou +3

Photographing optoelectronic displays often introduces unwanted moiré patterns due to analog signal interference between the pixel grids of the display and the camera sensor arrays…

cs.CV2023

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

Qiu Zhou, Jinming Cao, Hanchao Leng +3

In the field of autonomous driving, accurate and comprehensive perception of the 3D environment is crucial. Bird's Eye View (BEV) based methods have emerged as a promising solution…

cs.CV20211 cited

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

Jinming Cao, Hanchao Leng, Dani Lischinski +3

RGB-D semantic segmentation has attracted increasing attention over the past few years. Existing methods mostly employ homogeneous convolution operators to consume the RGB and dept…

cs.CV202040 cited

DO-Conv: Depthwise Over-parameterized Convolutional Layer

Jinming Cao, Yangyan Li, Mingchao Sun +5

Convolutional layers are the core building blocks of Convolutional Neural Networks (CNNs). In this paper, we propose to augment a convolutional layer with an additional depthwise c…

cs.CV2018

DiDA: Disentangled Synthesis for Domain Adaptation

Jinming Cao, Oren Katzir, Peng Jiang +4

Unsupervised domain adaptation aims at learning a shared model for two related, but not identical, domains by leveraging supervision from a source domain to an unsupervised target…