activity
20162022
most citedModel-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution Reconstruction

31 citations · 117 across the 16 of their papers we have counts for

collaborators

25 papers

cs.CV2022

Towards Real World HDRTV Reconstruction: A Data Synthesis-based Approach

Zhen Cheng, Tao Wang, Yong Li +3

Existing deep learning based HDRTV reconstruction methods assume one kind of tone mapping operators (TMOs) as the degradation procedure to synthesize SDRTV-HDRTV pairs for supervis…

eess.AS2022

TridentSE: Guiding Speech Enhancement with 32 Global Tokens

Dacheng Yin, Zhiyuan Zhao, Chuanxin Tang +2

In this paper, we present TridentSE, a novel architecture for speech enhancement, which is capable of efficiently capturing both global information and local details. TridentSE mai…

eess.IV202231 cited

Model-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution Reconstruction

Gang Yang, Li Zhang, Man Zhou +4

Magnetic resonance imaging (MRI) with high resolution (HR) provides more detailed information for accurate diagnosis and quantitative image analysis. Despite the significant advanc…

cs.CV20222 cited

Recurrent Dynamic Embedding for Video Object Segmentation

Mingxing Li, Li Hu, Zhiwei Xiong +3

Space-time memory (STM) based video object segmentation (VOS) networks usually keep increasing memory bank every several frames, which shows excellent performance. However, 1) the…

cs.CV2022

Degradation-agnostic Correspondence from Resolution-asymmetric Stereo

Xihao Chen, Zhiwei Xiong, Zhen Cheng +3

In this paper, we study the problem of stereo matching from a pair of images with different resolutions, e.g., those acquired with a tele-wide camera system. Due to the difficulty…

cs.LG20224 cited

Retriever: Learning Content-Style Representation as a Token-Level Bipartite Graph

Dacheng Yin, Xuanchi Ren, Chong Luo +3

This paper addresses the unsupervised learning of content-style decomposed representation. We first give a definition of style and then model the content-style representation as a…