40 citations · 65 across the 4 of their papers we have counts for
6 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.…
MeliusNet: Can Binary Neural Networks Achieve MobileNet-level Accuracy?
Joseph Bethge, Christian Bartz, Haojin Yang +2
Binary Neural Networks (BNNs) are neural networks which use binary weights and activations instead of the typical 32-bit floating point values. They have reduced model sizes and al…
Deep SCNN-based Real-time Object Detection for Self-driving Vehicles Using LiDAR Temporal Data
Shibo Zhou, Ying Chen, Xiaohua Li +1
Real-time accurate detection of three-dimensional (3D) objects is a fundamental necessity for self-driving vehicles. Most existing computer vision approaches are based on convoluti…
Single Image Super Resolution based on a Modified U-net with Mixed Gradient Loss
Zhengyang Lu, Ying Chen
Single image super-resolution (SISR) is the task of inferring a high-resolution image from a single low-resolution image. Recent research on super-resolution has achieved great pro…