4 accepted papers
Generalizable face anti-spoofing (FAS) based on domain generalization (DG) has gained growing attention due to its robustness in real-world applications. However, these DG methods rely heavily on labeled source data, which are usually costly and hard to access. Comparably, unlabeled face data are fa…
"Despite promising success in intra-dataset tests, existing face anti-spoofing (FAS) methods suffer from poor generalization ability under domain shift. This problem can be solved by aligning source and target data. However, due to privacy and security concerns of human faces, source data are usuall…
Bilinear pooling has been recently proposed as a feature encoding layer, which can be used after the convolutional layers of a deep network, to improve performance in multiple vision tasks. Different from conventional global average pooling or fully connected layer, bilinear pooling gathers 2nd o…
In this paper we propose a new framework to compare and classify temporal sequences. The proposed approach captures the underlying dynamics of the data while avoiding expensive estimation procedures, making it suitable to process large numbers of sequences. The main idea is to first embed the seque…