Predicting Human Interaction via Relative Attention Model

Predicting human interaction is challenging as the on-going activity has to be inferred based on a partially observed video. Essentially, a good algorithm should effectively model the mutual influence between the two interacting subjects. Also, only a small region in the scene is discriminative for identifying the on-going interaction. In this work, we propose a relative attention model to explicitly address these difficulties. Built on a tri-coupled deep recurrent structure representing both interacting subjects and global interaction status, the proposed network collects spatio-temporal information from each subject, rectified with global interaction information, yielding effective interaction representation. Moreover, the proposed network also unifies an attention module to assign higher importance to the regions which are relevant to the on-going action. Extensive experiments have been conducted on two public datasets, and the results demonstrate that the proposed relative attention network successfully predicts informative regions between interacting subjects, which in turn yields superior human interaction prediction accuracy.Comment: To appear in IJCAI 201

Skeleton-aided Articulated Motion Generation

This work make the first attempt to generate articulated human motion sequence from a single image. On the one hand, we utilize paired inputs including human skeleton information as motion embedding and a single human image as appearance reference, to generate novel motion frames, based on the conditional GAN infrastructure. On the other hand, a triplet loss is employed to pursue appearance-smoothness between consecutive frames. As the proposed framework is capable of jointly exploiting the image appearance space and articulated/kinematic motion space, it generates realistic articulated motion sequence, in contrast to most previous video generation methods which yield blurred motion effects. We test our model on two human action datasets including KTH and Human3.6M, and the proposed framework generates very promising results on both datasets.Comment: ACM MM 201

HIV-1 Gag-specific immunity induced by a lentivector-based vaccine directed to dendritic cells

Lentivectors (LVs) have attracted considerable interest for their potential as a vaccine delivery vehicle. In this study, we evaluate in mice a dendritic cell (DC)-directed LV system encoding the Gag protein of human immunodeficiency virus (HIV) (LV-Gag) as a potential vaccine for inducing an anti-HIV immune response. The DC-directed specificity is achieved through pseudotyping the vector with an engineered Sindbis virus glycoprotein capable of selectively binding to the DC-SIGN protein. A single immunization by this vector induces a durable HIV Gag-specific immune response. We investigated the antigen-specific immunity and T-cell memory generated by a prime/boost vaccine regimen delivered by either successive LV-Gag injections or a DNA prime/LV-Gag boost protocol. We found that both prime/boost regimens significantly enhance cellular and humoral immune responses. Importantly, a heterologous DNA prime/LV-Gag boost regimen results in superior Gag-specific T-cell responses as compared with a DNA prime/adenovector boost immunization. It induces not only a higher magnitude response, as measured by Gag-specific tetramer analysis and intracellular IFN-γ staining, but also a better quality of response evidenced by a wider mix of cytokines produced by the Gag-specific CD8^+ and CD4^+ T cells. A boosting immunization with LV-Gag also generates T cells reactive to a broader range of Gag-derived epitopes. These results demonstrate that this DC-directed LV immunization is a potent modality for eliciting anti-HIV immune responses