115 citations · 218 across the 7 of their papers we have counts for
8 papers · 1 filter
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…
GSTO: Gated Scale-Transfer Operation for Multi-Scale Feature Learning in Pixel Labeling
Zhuoying Wang, Yongtao Wang, Zhi Tang +4
Existing CNN-based methods for pixel labeling heavily depend on multi-scale features to meet the requirements of both semantic comprehension and detail preservation. State-of-the-a…
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…
Face Identity Disentanglement via Latent Space Mapping
Yotam Nitzan, Amit Bermano, Yangyan Li +1
Learning disentangled representations of data is a fundamental problem in artificial intelligence. Specifically, disentangled latent representations allow generative models to cont…
MixTConv: Mixed Temporal Convolutional Kernels for Efficient Action Recogntion
Kaiyu Shan, Yongtao Wang, Zhuoying Wang +4
To efficiently extract spatiotemporal features of video for action recognition, most state-of-the-art methods integrate 1D temporal convolution into a conventional 2D CNN backbone.…
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…