Search Results for author: ZhengJun Zha

Found 6 papers, 2 papers with code

Weakly Supervised High-Fidelity Clothing Model Generation

no code implementations14 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

Few-shot Learning with Global Relatedness Decoupled-Distillation

no code implementations12 Jul 2021 Yuan Zhou, Yanrong Guo, Shijie Hao, Richang Hong, ZhengJun Zha, Meng Wang

To overcome these problems, we propose a new Global Relatedness Decoupled-Distillation (GRDD) method using the global category knowledge and the Relatedness Decoupled-Distillation (RDD) strategy.

Few-Shot Learning Metric Learning

Low-Rank Subspaces in GANs

1 code implementation NeurIPS 2021 Jiapeng Zhu, Ruili Feng, Yujun Shen, Deli Zhao, ZhengJun Zha, Jingren Zhou, Qifeng Chen

Concretely, given an arbitrary image and a region of interest (e. g., eyes of face images), we manage to relate the latent space to the image region with the Jacobian matrix and then use low-rank factorization to discover steerable latent subspaces.

MVT: Mask Vision Transformer for Facial Expression Recognition in the wild

no code implementations8 Jun 2021 Hanting Li, Mingzhe Sui, Feng Zhao, ZhengJun Zha, Feng Wu

Facial Expression Recognition (FER) in the wild is an extremely challenging task in computer vision due to variant backgrounds, low-quality facial images, and the subjectiveness of annotators.

Facial Expression Recognition

On Noise Injection in Generative Adversarial Networks

2 code implementations10 Jun 2020 Ruili Feng, Deli Zhao, ZhengJun Zha

Noise injection has been proved to be one of the key technique advances in generating high-fidelity images.

Image Generation

Dense Residual Network: Enhancing Global Dense Feature Flow for Character Recognition

no code implementations23 Jan 2020 Zhao Zhang, Zemin Tang, Yang Wang, Zheng Zhang, Choujun Zhan, ZhengJun Zha, Meng Wang

To construct FDRN, we propose a new fast residual dense block (f-RDB) to retain the ability of local feature fusion and local residual learning of original RDB, which can reduce the computing efforts at the same time.

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