activity
20172026
most citedCross-Modality Deep Feature Learning for Brain Tumor Segmentation

290 citations · 692 across the 33 of their papers we have counts for

collaborators
Showing 2020Show all

6 papers · 1 filter

eess.IV2020★ 10 cited

Perception Consistency Ultrasound Image Super-resolution via Self-supervised CycleGAN

Heng Liu, Jianyong Liu, Tao Tao +2

Due to the limitations of sensors, the transmission medium and the intrinsic properties of ultrasound, the quality of ultrasound imaging is always not ideal, especially its low spa…

cs.LG2020

ResRep: Lossless CNN Pruning via Decoupling Remembering and Forgetting

Xiaohan Ding, Tianxiang Hao, Jianchao Tan +4

We propose ResRep, a novel method for lossless channel pruning (a.k.a. filter pruning), which slims down a CNN by reducing the width (number of output channels) of convolutional la…

cs.CV2020★ 24 cited

Shallow Feature Based Dense Attention Network for Crowd Counting

Yunqi Miao, Zijia Lin, Guiguang Ding +1

While the performance of crowd counting via deep learning has been improved dramatically in the recent years, it remains an ingrained problem due to cluttered backgrounds and varyi…

cs.CV2020

IMRAM: Iterative Matching with Recurrent Attention Memory for Cross-Modal Image-Text Retrieval

Hui Chen, Guiguang Ding, Xudong Liu +3

Enabling bi-directional retrieval of images and texts is important for understanding the correspondence between vision and language. Existing methods leverage the attention mechani…

cs.CV2020

NAS-Count: Counting-by-Density with Neural Architecture Search

Yutao Hu, Xiaolong Jiang, Xuhui Liu +4

Most of the recent advances in crowd counting have evolved from hand-designed density estimation networks, where multi-scale features are leveraged to address the scale variation p…

eess.IV2020

2.75D: Boosting learning by representing 3D Medical imaging to 2D features for small data

Xin Wang, Ruisheng Su, Weiyi Xie +5

In medical-data driven learning, 3D convolutional neural networks (CNNs) have started to show superior performance to 2D CNNs in numerous deep learning tasks, proving the added val…