Attention-Aware Face Hallucination via Deep Reinforcement Learning

Face hallucination is a domain-specific super-resolution problem with the goal to generate high-resolution (HR) faces from low-resolution (LR) input images. In contrast to existing methods that often learn a single patch-to-patch mapping from LR to HR images and are regardless of the contextual interdependency between patches, we propose a novel Attention-aware Face Hallucination (Attention-FH) framework which resorts to deep reinforcement learning for sequentially discovering attended patches and then performing the facial part enhancement by fully exploiting the global interdependency of the image. Specifically, in each time step, the recurrent policy network is proposed to dynamically specify a new attended region by incorporating what happened in the past. The state (i.e., face hallucination result for the whole image) can thus be exploited and updated by the local enhancement network on the selected region. The Attention-FH approach jointly learns the recurrent policy network and local enhancement network through maximizing the long-term reward that reflects the hallucination performance over the whole image. Therefore, our proposed Attention-FH is capable of adaptively personalizing an optimal searching path for each face image according to its own characteristic. Extensive experiments show our approach significantly surpasses the state-of-the-arts on in-the-wild faces with large pose and illumination variations

Popular Opinion Leader intervention for HIV stigma reduction in health care settings.

This study used the Popular Opinion Leader (POL) model to reduce stigma among service providers. The authors focused on the dissemination of intervention messages from trained POL providers to their peer providers and the change of intervention outcome over time. The sample included 880 service providers from 20 intervention hospitals. The levels of message diffusion, prejudicial attitude toward people living with HIV (PLH), and avoidance intent to serve PLH were self-reported at baseline, 6 months, and 12 months. At 6 months, POL providers showed a significantly higher level of message diffusion and lower levels of prejudicial attitude and avoidance intent than non-POL providers. However, such discrepancies diminished at 12 months. The results support the utility of the POL model in stigma reduction interventions. The observed changes were documented not only in POLs but also in non-POLs after a certain period of time. This finding informed the design and implementation of future stigma reduction efforts and POL intervention programs

Gender Differences in Depressive Symptoms Among HIV-Positive Concordant and Discordant Heterosexual Couples in China.

HIV seropositive individuals and their heterosexual partners/spouses, either seropositive or seronegative, are facing several mental health challenges. The objective of this study was to examine gender differences in depressive symptoms among HIV-positive concordant and HIV-discordant couples. We identified heterosexual couples from participants of a randomized controlled trial conducted in Anhui province, China. A total of 265 couples, comprising 129 HIV+ male/HIV- female couples, 98 HIV- male/HIV+ female couples, and 38 HIV-positive concordant couples, were included in the analyses. We collected data using the computer-assisted personal interview method. We used a linear mixed-effects regression model to assess whether gender differences in depressive symptoms varied across couple types. HIV-positive women reported a significantly higher level of depressive symptoms than their partners/spouses. HIV-positive women with HIV-positive partners had higher depressive symptoms than those with HIV-negative partners, whereas HIV-positive men reported similar levels of depressive symptoms regardless of their partners' serostatus. Among the concordant couples, those with the highest annual family income showed the greatest gender differences in depressive symptoms. We suggest that family interventions should be gender- and couple-type specific and that mental health counseling is warranted not only for HIV-positive women but also for HIV-negative women in an HIV-affected relationship

Instance-Level Salient Object Segmentation

Image saliency detection has recently witnessed rapid progress due to deep convolutional neural networks. However, none of the existing methods is able to identify object instances in the detected salient regions. In this paper, we present a salient instance segmentation method that produces a saliency mask with distinct object instance labels for an input image. Our method consists of three steps, estimating saliency map, detecting salient object contours and identifying salient object instances. For the first two steps, we propose a multiscale saliency refinement network, which generates high-quality salient region masks and salient object contours. Once integrated with multiscale combinatorial grouping and a MAP-based subset optimization framework, our method can generate very promising salient object instance segmentation results. To promote further research and evaluation of salient instance segmentation, we also construct a new database of 1000 images and their pixelwise salient instance annotations. Experimental results demonstrate that our proposed method is capable of achieving state-of-the-art performance on all public benchmarks for salient region detection as well as on our new dataset for salient instance segmentation.Comment: To appear in CVPR201