activity
20182022
most citedMeliusNet: Can Binary Neural Networks Achieve MobileNet-level Accuracy?

74 citations · 233 across the 15 of their papers we have counts for

collaborators
Showing 2020Show all

8 papers · 1 filter

cs.CV2020★ 1 cited

Domain Adaptation on Semantic Segmentation for Aerial Images

Ying Chen, Xu Ouyang, Kaiyue Zhu +1

Semantic segmentation has achieved significant advances in recent years. While deep neural networks perform semantic segmentation well, their success rely on pixel level supervisio…

cs.CV2020★ 3 cited

MANGO: A Mask Attention Guided One-Stage Scene Text Spotter

Liang Qiao, Ying Chen, Zhanzhan Cheng +4

Recently end-to-end scene text spotting has become a popular research topic due to its advantages of global optimization and high maintainability in real applications. Most methods…

cs.CV2020★ 1 cited

Cascade Attentive Dropout for Weakly Supervised Object Detection

Wenlong Gao, Ying Chen, Yong Peng

Weakly supervised object detection (WSOD) aims to classify and locate objects with only image-level supervision. Many WSOD approaches adopt multiple instance learning as the initia…

eess.IV2020★ 2 cited

Dense U-net for super-resolution with shuffle pooling layer

Zhengyang Lu, Ying Chen

Recent researches have achieved great progress on single image super-resolution(SISR) due to the development of deep learning in the field of computer vision. In these method, the…

cs.CV2020

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…

cs.CV2020★ 40 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…