Search Results for author: Cheng Ma

Found 8 papers, 5 papers with code

Weakly Supervised High-Fidelity Clothing Model Generation

1 code implementation14 Dec 2021 Ruili Feng, Cheng Ma, Chengji Shen, Xin Gao, Zhenjiang Liu, Xiaobo Li, Kairi Ou, ZhengJun Zha

The development of online economics arouses the demand of generating images of models on product clothes, to display new clothes and promote sales.

Virtual Try-on

Structure-Preserving Image Super-Resolution

1 code implementation26 Sep 2021 Cheng Ma, Yongming Rao, Jiwen Lu, Jie zhou

Firstly, we propose SPSR with gradient guidance (SPSR-G) by exploiting gradient maps of images to guide the recovery in two aspects.

Image Super-Resolution SSIM

Rank-Consistency Deep Hashing for Scalable Multi-Label Image Search

no code implementations2 Feb 2021 Cheng Ma, Jiwen Lu, Jie zhou

As hashing becomes an increasingly appealing technique for large-scale image retrieval, multi-label hashing is also attracting more attention for the ability to exploit multi-level semantic contents.

Image Retrieval Semantic Similarity +1

Universality of noise-induced resilience restoration in spatially-extended ecological systems

no code implementations24 Nov 2020 Cheng Ma, Gyorgy Korniss, Boleslaw K. Szymanski, Jianxi Gao

Resilience restoration focuses on the ability of spatially-extended systems and the required time to recover to their desired states under stochastic environmental conditions.

Deep Face Super-Resolution with Iterative Collaboration between Attentive Recovery and Landmark Estimation

1 code implementation CVPR 2020 Cheng Ma, Zhenyu Jiang, Yongming Rao, Jiwen Lu, Jie zhou

In this paper, we propose a deep face super-resolution (FSR) method with iterative collaboration between two recurrent networks which focus on facial image recovery and landmark estimation respectively.


Structure-Preserving Super Resolution with Gradient Guidance

2 code implementations CVPR 2020 Cheng Ma, Yongming Rao, Yean Cheng, Ce Chen, Jiwen Lu, Jie zhou

In this paper, we propose a structure-preserving super resolution method to alleviate the above issue while maintaining the merits of GAN-based methods to generate perceptual-pleasant details.

Image Super-Resolution SSIM

Transfer Learning in General Lensless Imaging through Scattering Media

no code implementations28 Dec 2019 Yukuan Yang, Lei Deng, Peng Jiao, Yansong Chua, Jing Pei, Cheng Ma, Guoqi Li

In summary, this work provides a new solution for lensless imaging through scattering media using transfer learning in DNNs.

Transfer Learning

Multi-Context Attention for Human Pose Estimation

2 code implementations CVPR 2017 Xiao Chu, Wei Yang, Wanli Ouyang, Cheng Ma, Alan L. Yuille, Xiaogang Wang

We further combine the holistic attention model, which focuses on the global consistency of the full human body, and the body part attention model, which focuses on the detailed description for different body parts.

Pose Estimation

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