Joint-SRVDNet: Joint Super Resolution and Vehicle Detection Network

In many domestic and military applications, aerial vehicle detection and super-resolutionalgorithms are frequently developed and applied independently. However, aerial vehicle detection on super-resolved images remains a challenging task due to the lack of discriminative information in the super-resolved images. To address this problem, we propose a Joint Super-Resolution and Vehicle DetectionNetwork (Joint-SRVDNet) that tries to generate discriminative, high-resolution images of vehicles fromlow-resolution aerial images. First, aerial images are up-scaled by a factor of 4x using a Multi-scaleGenerative Adversarial Network (MsGAN), which has multiple intermediate outputs with increasingresolutions. Second, a detector is trained on super-resolved images that are upscaled by factor 4x usingMsGAN architecture and finally, the detection loss is minimized jointly with the super-resolution loss toencourage the target detector to be sensitive to the subsequent super-resolution training. The network jointlylearns hierarchical and discriminative features of targets and produces optimal super-resolution results. Weperform both quantitative and qualitative evaluation of our proposed network on VEDAI, xView and DOTAdatasets. The experimental results show that our proposed framework achieves better visual quality than thestate-of-the-art methods for aerial super-resolution with 4x up-scaling factor and improves the accuracy ofaerial vehicle detection

Polarimetric Thermal to Visible Face Verification via Self-Attention Guided Synthesis

Polarimetric thermal to visible face verification entails matching two images that contain significant domain differences. Several recent approaches have attempted to synthesize visible faces from thermal images for cross-modal matching. In this paper, we take a different approach in which rather than focusing only on synthesizing visible faces from thermal faces, we also propose to synthesize thermal faces from visible faces. Our intuition is based on the fact that thermal images also contain some discriminative information about the person for verification. Deep features from a pre-trained Convolutional Neural Network (CNN) are extracted from the original as well as the synthesized images. These features are then fused to generate a template which is then used for verification. The proposed synthesis network is based on the self-attention generative adversarial network (SAGAN) which essentially allows efficient attention-guided image synthesis. Extensive experiments on the ARL polarimetric thermal face dataset demonstrate that the proposed method achieves state-of-the-art performance.Comment: This work is accepted at the 12th IAPR International Conference On Biometrics (ICB 2019

Locating the ‘radical’ in 'Shoot the Messenger'

This is the author's accepted manuscript. The final published article is available from the link below, copyright 2013 @ Edinburgh University Press.The 2006 BBC drama Shoot the Messenger is based on the psychological journey of a Black schoolteacher, Joe Pascale, accused of assaulting a Black male pupil. The allegation triggers Joe's mental breakdown which is articulated, through Joe's first-person narration, as a vindictive loathing of Black people. In turn, a range of common stereotypical characterisations and discourses based on a Black culture of hypocrisy, blame and entitlement is presented. The text is therefore laid wide open to a critique of its neo-conservatism and hegemonic narratives of Black Britishness. However, the drama's presentation of Black mental illness suggests that Shoot the Messenger may also be interpreted as a critique of social inequality and the destabilising effects of living with ethnicised social categories. Through an analysis of issues of representation, the article reclaims this controversial text as a radical drama and examines its implications for and within a critical cultural politics of ‘race’ and representation

Mouse cytomegalovirus-experienced ILC1s acquire a memory response dependent on the viral glycoprotein m12.

Innate lymphoid cells (ILCs) are tissue-resident sentinels that are essential for early host protection from pathogens at initial sites of infection. However, whether pathogen-derived antigens directly modulate the responses of tissue-resident ILCs has remained unclear. In the present study, it was found that liver-resident type 1 ILCs (ILC1s) expanded locally and persisted after the resolution of infection with mouse cytomegalovirus (MCMV). ILC1s acquired stable transcriptional, epigenetic and phenotypic changes a month after the resolution of MCMV infection, and showed an enhanced protective effector response to secondary challenge with MCMV consistent with a memory lymphocyte response. Memory ILC1 responses were dependent on the MCMV-encoded glycoprotein m12, and were independent of bystander activation by proinflammatory cytokines after heterologous infection. Thus, liver ILC1s acquire adaptive features in an MCMV-specific manner