15 citations · 29 across the 6 of their papers we have counts for
6 papers
A Novel Framework for Image-to-image Translation and Image Compression
Fei Yang, Yaxing Wang, Luis Herranz +2
Data-driven paradigms using machine learning are becoming ubiquitous in image processing and communications. In particular, image-to-image (I2I) translation is a generic and widely…
3D Shapes Local Geometry Codes Learning with SDF
Shun Yao, Fei Yang, Yongmei Cheng +1
A signed distance function (SDF) as the 3D shape description is one of the most effective approaches to represent 3D geometry for rendering and reconstruction. Our work is inspired…
Slimmable Compressive Autoencoders for Practical Neural Image Compression
Fei Yang, Luis Herranz, Yongmei Cheng +1
Neural image compression leverages deep neural networks to outperform traditional image codecs in rate-distortion performance. However, the resulting models are also heavy, computa…
Semantic Drift Compensation for Class-Incremental Learning
Lu Yu, Bartłomiej Twardowski, Xialei Liu +5
Class-incremental learning of deep networks sequentially increases the number of classes to be classified. During training, the network has only access to data of one task at a tim…
Learning Metrics from Teachers: Compact Networks for Image Embedding
Lu Yu, Vacit Oguz Yazici, Xialei Liu +3
Metric learning networks are used to compute image embeddings, which are widely used in many applications such as image retrieval and face recognition. In this paper, we propose to…
Weakly Supervised Domain-Specific Color Naming Based on Attention
Lu Yu, Yongmei Cheng, Joost van de Weijer
The majority of existing color naming methods focuses on the eleven basic color terms of the English language. However, in many applications, different sets of color names are used…