Differential mucosal tropism and dissemination of classical and hypervirulent Klebsiella pneumoniae infection.

Klebsiella pneumoniae (Kp) infection is an important healthcare concern. The ST258 classical (c)Kp strain is dominant in hospital-acquired infections in North America and Europe, while ST23 hypervirulent (hv)Kp prevails in community-acquired infections in Asia. This study aimed to develop symptomatic mucosal infection models in mice that mirror natural infections in humans to gain a deeper understanding of Kp mucosal pathogenesis. We showed that cKp replicates in the nasal cavity instead of the lungs, and this early infection event is crucial for the establishment of chronic colonization in the cecum and colon. In contrast, hvKp replicates directly in the lungs to lethal bacterial load, and early infection of esophagus supported downstream transient colonization in the ileum and cecum. Here, we have developed an in vivo model that illuminates how differences in Kp tropism are responsible for virulence and disease phenotype in cKp and hvKp, providing the basis for further mechanistic study.

Interactive Object Segmentation With Inside-Outside Guidance.

This article explores how to harvest precise object segmentation masks while minimizing the human interaction cost. To achieve this, we propose a simple yet effective interaction scheme, named Inside-Outside Guidance (IOG). Concretely, we leverage an inside point that is clicked near the object center and two outside points at the symmetrical corner locations (top-left and bottom-right or top-right and bottom-left) of an almost-tight bounding box that encloses the target object. The interaction results in a total of one foreground click and four background clicks for segmentation. The advantages of our IOG are four-fold: 1) the two outside points can help remove distractions from other objects or background; 2) the inside point can help eliminate the unrelated regions inside the bounding box; 3) the inside and outside points are easily identified, reducing the confusion raised by the state-of-the-art DEXTR Maninis et al. 2018, in labeling some extreme samples; 4) it naturally supports additional click annotations for further correction. Despite its simplicity, our IOG not only achieves state-of-the-art performance on several popular benchmarks such as GrabCut Rother et al. 2004, PASCAL Everingham et al. 2010 and MS COCO Russakovsky et al. 2015, but also demonstrates strong generalization capability across different domains such as street scenes (Cityscapes Cordts et al. 2016), aerial imagery (Rooftop Sun et al. 2014 and Agriculture-Vision Chiu et al. 2020) and medical images (ssTEM Gerhard et al. 2013). Code is available at https://github.com/shiyinzhang/Inside-Outside-Guidancehttps://github.com/shiyinzhang/Inside-Outside-Guidance.

SODAR: Segmenting Objects by Dynamically Aggregating Neighboring Mask Representations.

Recent state-of-the-art one-stage instance segmentation model SOLO divides the input image into a grid and directly predicts per grid cell object masks with fully-convolutional networks, yielding comparably good performance as traditional two-stage Mask R-CNN yet enjoying much simpler architecture and higher efficiency. We observe SOLO generates similar masks for an object at nearby grid cells, and these neighboring predictions can complement each other as some may better segment certain object part, most of which are however directly discarded by non-maximum-suppression. Motivated by the observed gap, we develop a novel learning-based aggregation method that improves upon SOLO by leveraging the rich neighboring information while maintaining the architectural efficiency. The resulting model is named SODAR. Unlike the original per grid cell object masks, SODAR is implicitly supervised to learn mask representations that encode geometric structure of nearby objects and complement adjacent representations with context. The aggregation method further includes two novel designs: 1) a mask interpolation mechanism that enables the model to generate much fewer mask representations by sharing neighboring representations among nearby grid cells, and thus saves computation and memory; 2) a deformable neighbour sampling mechanism that allows the model to adaptively adjust neighbor sampling locations thus gathering mask representations with more relevant context and achieving higher performance. SODAR significantly improves the instance segmentation performance, e.g., it outperforms a SOLO model with ResNet-101 backbone by 2.2 AP on COCO test set, with only about 3% additional computation. We further show consistent performance gain with the SOLOv2 model.

Cross-Layer Feature Pyramid Network for Salient Object Detection.

Feature pyramid network (FPN) based models, which fuse the semantics and salient details in a progressive manner, have been proven highly effective in salient object detection. However, it is observed that these models often generate saliency maps with incomplete object structures or unclear object boundaries, due to the indirect information propagation among distant layers that makes such fusion structure less effective. In this work, we propose a novel Cross-layer Feature Pyramid Network (CFPN), in which direct cross-layer communication is enabled to improve the progressive fusion in salient object detection. Specifically, the proposed network first aggregates multi-scale features from different layers into feature maps that have access to both the high- and low- level information. Then, it distributes the aggregated features to all the involved layers to gain access to richer context. In this way, the distributed features per layer own both semantics and salient details from all other layers simultaneously, and suffer reduced loss of important information during the progressive feature fusion. At last, CFPN fuses the distributed features of each layer stage-by-stage. This way, the high-level features that contain context useful for locating complete objects are preserved until the final output layer, and the low-level features that contain spatial structure details are embedded into each layer to preserve spatial structural details. Extensive experimental results over six widely used salient object detection benchmarks and with three popular backbones clearly demonstrate that CFPN can accurately locate fairly complete salient regions and effectively segment the object boundaries.