913 citations · 3.2k across the 60 of their papers we have counts for
6 papers · 1 filter
Partial Convolution based Padding
Guilin Liu, Kevin J. Shih, Ting-Chun Wang +5
In this paper, we present a simple yet effective padding scheme that can be used as a drop-in module for existing convolutional neural networks. We call it partial convolution base…
Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training
Yang Zou, Zhiding Yu, B. V. K. Vijaya Kumar +1
Recent deep networks achieved state of the art performance on a variety of semantic segmentation tasks. Despite such progress, these models often face challenges in real world `wil…
Simultaneous Edge Alignment and Learning
Zhiding Yu, Weiyang Liu, Yang Zou +4
Edge detection is among the most fundamental vision problems for its role in perceptual grouping and its wide applications. Recent advances in representation learning have led to c…
Learning towards Minimum Hyperspherical Energy
Weiyang Liu, Rongmei Lin, Zhen Liu +4
Neural networks are a powerful class of nonlinear functions that can be trained end-to-end on various applications. While the over-parametrization nature in many neural networks re…
Decoupled Networks
Weiyang Liu, Zhen Liu, Zhiding Yu +5
Inner product-based convolution has been a central component of convolutional neural networks (CNNs) and the key to learning visual representations. Inspired by the observation tha…
Learning Strict Identity Mappings in Deep Residual Networks
Xin Yu, Zhiding Yu, Srikumar Ramalingam
A family of super deep networks, referred to as residual networks or ResNet, achieved record-beating performance in various visual tasks such as image recognition, object detection…