6 accepted papers
Video de-raining is an important yet highly challenging task in the field of computer vision. Though numerous video de-raining methods are developed with encouraging performance, two major challenges for video de-raining are still unsatisfactorily solved and need to be further investigated as follow…
Most recognizing occluded faces methods focus on synthetic-occluded faces for training due to the lack of real-occluded data. However, the performance may suffer from degradation since the synthetic-occluded and real-occluded face images are under different distributions. Hence, it draws our eyes to…
Conventional face anti-spoofing methods might be poorly generalized to unseen data distributions. Thus, we improve the generalization of spoof detection from the multi-domain feature disentanglement. Specially, a two-branch convolutional network is proposed to separate spoof-specific features and do…
Rain removal is a highly demanding task because a rainy image in computer lacks discriminative information to distinguish the image details from the rain streaks. In this paper, we present a new High-Low-Frequency Guided De-raining (HLFGD) method to remove the rain streaks clearly while reserve the…
Most of the face anti-spoofing methods improve the generalization capability by adversarial domain adaptation via training the source and target domain data jointly. However, considering the data privacy, it is impractical in application. Hence, we propose a source data-free domain adaptative face a…
3D Morphable Model (3DMM) is a statistical tool widely employed in reconstructing 3D face shape. Existing methods are aimed at predicting 3DMM shape parameters with a single encoder but suffer from unclear distinction of different attributes. To address this problem, Two-Pathway Encoder-Decoder Netw…