6 accepted papers
Due to privacy and cost issues, the lack of large-scale labeled datasets limits the advancement of person re-identification. Existing methods use generative adversarial networks or game engine rendering for data augmentation to improve re-identification performance. However, these approaches struggl…
In recent years, methods based on diffusion generative models have achieved state-of-the-art performances in voice generation. Most of these previous approaches are based on first-order stochastic differential equations or their equivalent diffusion models. This paper attempts to upgrade these first…
In recent years, score-based generative models (SGM) have achieved state-of-the-art (SOTA) performance in noisy image restoration [1], [2]. But at present, most of these methods are performed in the position space, and there is a lack in modeling of the velocity and acceleration of the image on the…
This paper presents a novel facial action unit (AU) detection method by simultaneously improving AU feature’s discriminative ability and alleviating the AU data scarcity problem. We design a supervised AU soft mask attention scheme to learn local AU features by integrating prior expert knowledge. To…
Deep gated convolutional networks have been proved to be very effective in single channel speech separation. However current state-of-the-art framework often considers training the gated convolutional networks in time-frequency (TF) domain. Such an approach will result in limited perceptual score, s…
Face attributes are interesting due to their detailed description of human faces. Unlike prior researches working on attribute prediction, we address an inverse and more challenging problem called face attribute manipulation which aims at modifying a face image according to a given attribute value.…