142 citations · 332 across the 8 of their papers we have counts for
9 papers
Scanpath Prediction in Panoramic Videos via Expected Code Length Minimization
Mu Li, Kanglong Fan, Kede Ma
Predicting human scanpaths when exploring panoramic videos is a challenging task due to the spherical geometry and the multimodality of the input, and the inherent uncertainty and…
Pseudocylindrical Convolutions for Learned Omnidirectional Image Compression
Mu Li, Kede Ma, Jinxing Li +1
Although equirectangular projection (ERP) is a convenient form to store omnidirectional images (also known as 360-degree images), it is neither equal-area nor conformal, thus not f…
Learning Context-Based Non-local Entropy Modeling for Image Compression
Mu Li, Kai Zhang, Wangmeng Zuo +2
The entropy of the codes usually serves as the rate loss in the recent learned lossy image compression methods. Precise estimation of the probabilistic distribution of the codes pl…
Efficient and Effective Context-Based Convolutional Entropy Modeling for Image Compression
Mu Li, Kede Ma, Jane You +2
Precise estimation of the probabilistic structure of natural images plays an essential role in image compression. Despite the recent remarkable success of end-to-end optimized imag…
Learning Content-Weighted Deep Image Compression
Mu Li, Wangmeng Zuo, Shuhang Gu +2
Learning-based lossy image compression usually involves the joint optimization of rate-distortion performance. Most existing methods adopt spatially invariant bit length allocation…
Shift-Net: Image Inpainting via Deep Feature Rearrangement
Zhaoyi Yan, Xiaoming Li, Mu Li +2
Deep convolutional networks (CNNs) have exhibited their potential in image inpainting for producing plausible results. However, in most existing methods, e.g., context encoder, the…
Enlarging Context with Low Cost: Efficient Arithmetic Coding with Trimmed Convolution
Mu Li, Shuhang Gu, David Zhang +1
Arithmetic coding is an essential class of coding techniques. One key issue of arithmetic encoding method is to predict the probability of the current coding symbol from its contex…
Deep Identity-aware Transfer of Facial Attributes
Mu Li, Wangmeng Zuo, David Zhang
This paper presents a Deep convolutional network model for Identity-Aware Transfer (DIAT) of facial attributes. Given the source input image and the reference attribute, DIAT aims…
Convolutional Network for Attribute-driven and Identity-preserving Human Face Generation
Mu Li, Wangmeng Zuo, David Zhang
This paper focuses on the problem of generating human face pictures from specific attributes. The existing CNN-based face generation models, however, either ignore the identity of…