6 accepted papers
The low resolution (LR) problem is rather challenging in face analysis. Most existing face hallucination methods assume that LR face images have only one resolution, but multiple resolutions may be available from different sources. To solve this issue, we propose a novel simultaneous facial canonica…
Conventional image set methods typically learn from image sets stored in one location. However, in real-world applications, image sets are often distributed or collected across different positions. Learning from such distributed image sets presents a challenge that has not been studied thus far. Mor…
Face super-resolution (FSR) is a powerful technique for restoring high-resolution face images from the captured low-resolution ones with the assistance of prior information. Existing FSR methods based on explicit or implicit covariance matrices are difficult to reveal complex nonlinear relationships…
Existing lexical substitution (LS) benchmarks were collected by asking human annotators to think of substitutes from memory, resulting in benchmarks with limited coverage and relatively small scales. To overcome this problem, we propose a novel annotation method to construct an LS dataset based on h…
Canonical correlation analysis (CCA) is a classical yet powerful tool for learning two-view feature representation in various fields. But, most CCA approaches are based on the conventional covariance measure, which makes them difficult to uncover the complicatedly nonlinear relationship between dist…
Canonical correlation analysis (CCA) matters in multiview representation learning. But, CCA and its most variants are essentially based on explicit or implicit covariance matrices. It means that they have no ability to model the nonlinear relationship among features due to intrinsic linearity of cov…