115 citations · 209 across the 5 of their papers we have counts for
5 papers
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…
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.…
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
Li Yi, Lin Shao, Manolis Savva +47
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database. The benchmark consists of two tasks: part-level segmentation…
Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views
Hao Su, Charles R. Qi, Yangyan Li +1
Object viewpoint estimation from 2D images is an essential task in computer vision. However, two issues hinder its progress: scarcity of training data with viewpoint annotations, a…