Self-Calibrated Cross Attention Network for Few-Shot Segmentation

The key to the success of few-shot segmentation (FSS) lies in how to effectively utilize support samples. Most solutions compress support foreground (FG) features into prototypes, but lose some spatial details. Instead, others use cross attention to fuse query features with uncompressed support FG. Query FG could be fused with support FG, however, query background (BG) cannot find matched BG features in support FG, yet inevitably integrates dissimilar features. Besides, as both query FG and BG are combined with support FG, they get entangled, thereby leading to ineffective segmentation. To cope with these issues, we design a self-calibrated cross attention (SCCA) block. For efficient patch-based attention, query and support features are firstly split into patches. Then, we design a patch alignment module to align each query patch with its most similar support patch for better cross attention. Specifically, SCCA takes a query patch as Q, and groups the patches from the same query image and the aligned patches from the support image as K&V. In this way, the query BG features are fused with matched BG features (from query patches), and thus the aforementioned issues will be mitigated. Moreover, when calculating SCCA, we design a scaled-cosine mechanism to better utilize the support features for similarity calculation. Extensive experiments conducted on PASCAL-5^i and COCO-20^i demonstrate the superiority of our model, e.g., the mIoU score under 5-shot setting on COCO-20^i is 5.6%+ better than previous state-of-the-arts. The code is available at https://github.com/Sam1224/SCCAN.Comment: This paper is accepted by ICCV'2

The Application of Carbon Footprint Analysis in Hunan Province

Based on interpreting carbon footprint’s definition and its effecting factors, making positive analyses by using the data of cities in Hunan Province from 2005 to 2009, this paper constructs the calculating model of carbon footprint and analyses the relationship between carbon footprint and population, economy development level, industrial structure and energy structure. Meanwhile, on the basis of above analyses, this paper puts forward effective ways to advance the low-carbon development of Hunan Province from four aspects

Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentation

Current methods for few-shot segmentation (FSSeg) have mainly focused on improving the performance of novel classes while neglecting the performance of base classes. To overcome this limitation, the task of generalized few-shot semantic segmentation (GFSSeg) has been introduced, aiming to predict segmentation masks for both base and novel classes. However, the current prototype-based methods do not explicitly consider the relationship between base and novel classes when updating prototypes, leading to a limited performance in identifying true categories. To address this challenge, we propose a class contrastive loss and a class relationship loss to regulate prototype updates and encourage a large distance between prototypes from different classes, thus distinguishing the classes from each other while maintaining the performance of the base classes. Our proposed approach achieves new state-of-the-art performance for the generalized few-shot segmentation task on PASCAL VOC and MS COCO datasets

IL-17A Synergizes with IFN-γ to Upregulate iNOS and NO Production and Inhibit Chlamydial Growth

IFN-γ-mediated inducible nitric oxide synthase (iNOS) expression is critical for controlling chlamydial infection through microbicidal nitric oxide (NO) production. Interleukin-17A (IL-17A), as a new proinflammatory cytokine, has been shown to play a protective role in host defense against Chlamydia muridarum (Cm) infection. To define the related mechanism, we investigated, in the present study, the effect of IL-17A on IFN-γ induced iNOS expression and NO production during Cm infection in vitro and in vivo. Our data showed that IL-17A significantly enhanced IFN-γ-induced iNOS expression and NO production and inhibited Cm growth in Cm-infected murine lung epithelial (TC-1) cells. The synergistic effect of IL-17A and IFN-γ on Chlamydia clearance from TC-1 cells correlated with iNOS induction. Since one of the main antimicrobial mechanisms of activated macrophages is the release of NO, we also examined the inhibitory effect of IL-17A and IFN-γ on Cm growth in peritoneal macrophages. IL-17A (10 ng/ml) synergizes with IFN-γ (200 U/ml) in macrophages to inhibit Cm growth. This effect was largely reversed by aminoguanidine (AG), an iNOS inhibitor. Finally, neutralization of IL-17A in Cm infected mice resulted in reduced iNOS expression in the lung and higher Cm growth. Taken together, the results indicate that IL-17A and IFN-γ play a synergistic role in inhibiting chlamydial lung infection, at least partially through enhancing iNOS expression and NO production in epithelial cells and macrophages