Towards Robust Few-shot Point Cloud Semantic Segmentation

Few-shot point cloud semantic segmentation aims to train a model to quickly adapt to new unseen classes with only a handful of support set samples. However, the noise-free assumption in the support set can be easily violated in many practical real-world settings. In this paper, we focus on improving the robustness of few-shot point cloud segmentation under the detrimental influence of noisy support sets during testing time. To this end, we first propose a Component-level Clean Noise Separation (CCNS) representation learning to learn discriminative feature representations that separates the clean samples of the target classes from the noisy samples. Leveraging the well separated clean and noisy support samples from our CCNS, we further propose a Multi-scale Degree-based Noise Suppression (MDNS) scheme to remove the noisy shots from the support set. We conduct extensive experiments on various noise settings on two benchmark datasets. Our results show that the combination of CCNS and MDNS significantly improves the performance. Our code is available at https://github.com/Pixie8888/R3DFSSeg.Comment: BMVC 202

Synthetic-to-Real Domain Generalized Semantic Segmentation for 3D Indoor Point Clouds

Semantic segmentation in 3D indoor scenes has achieved remarkable performance under the supervision of large-scale annotated data. However, previous works rely on the assumption that the training and testing data are of the same distribution, which may suffer from performance degradation when evaluated on the out-of-distribution scenes. To alleviate the annotation cost and the performance degradation, this paper introduces the synthetic-to-real domain generalization setting to this task. Specifically, the domain gap between synthetic and real-world point cloud data mainly lies in the different layouts and point patterns. To address these problems, we first propose a clustering instance mix (CINMix) augmentation technique to diversify the layouts of the source data. In addition, we augment the point patterns of the source data and introduce non-parametric multi-prototypes to ameliorate the intra-class variance enlarged by the augmented point patterns. The multi-prototypes can model the intra-class variance and rectify the global classifier in both training and inference stages. Experiments on the synthetic-to-real benchmark demonstrate that both CINMix and multi-prototypes can narrow the distribution gap and thus improve the generalization ability on real-world datasets

Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images

Semantic segmentation models based on convolutional neural networks (CNNs) have gained much attention in relation to remote sensing and have achieved remarkable performance for the extraction of buildings from high-resolution aerial images. However, the issue of limited generalization for unseen images remains. When there is a domain gap between the training and test datasets, CNN-based segmentation models trained by a training dataset fail to segment buildings for the test dataset. In this paper, we propose segmentation networks based on a domain adaptive transfer attack (DATA) scheme for building extraction from aerial images. The proposed system combines the domain transfer and adversarial attack concepts. Based on the DATA scheme, the distribution of the input images can be shifted to that of the target images while turning images into adversarial examples against a target network. Defending adversarial examples adapted to the target domain can overcome the performance degradation due to the domain gap and increase the robustness of the segmentation model. Cross-dataset experiments and the ablation study are conducted for the three different datasets: the Inria aerial image labeling dataset, the Massachusetts building dataset, and the WHU East Asia dataset. Compared to the performance of the segmentation network without the DATA scheme, the proposed method shows improvements in the overall IoU. Moreover, it is verified that the proposed method outperforms even when compared to feature adaptation (FA) and output space adaptation (OSA).Comment: 11pages, 12 figure

Generalized Few-Shot Point Cloud Segmentation Via Geometric Words

Existing fully-supervised point cloud segmentation methods suffer in the dynamic testing environment with emerging new classes. Few-shot point cloud segmentation algorithms address this problem by learning to adapt to new classes at the sacrifice of segmentation accuracy for the base classes, which severely impedes its practicality. This largely motivates us to present the first attempt at a more practical paradigm of generalized few-shot point cloud segmentation, which requires the model to generalize to new categories with only a few support point clouds and simultaneously retain the capability to segment base classes. We propose the geometric words to represent geometric components shared between the base and novel classes, and incorporate them into a novel geometric-aware semantic representation to facilitate better generalization to the new classes without forgetting the old ones. Moreover, we introduce geometric prototypes to guide the segmentation with geometric prior knowledge. Extensive experiments on S3DIS and ScanNet consistently illustrate the superior performance of our method over baseline methods. Our code is available at: https://github.com/Pixie8888/GFS-3DSeg_GWs.Comment: Accepted by ICCV 202

17β-estradiol reduces inflammation and modulates antioxidant enzymes in colonic epithelial cells

Background/Aims: Estrogen is known to have protective effect in colorectal cancer development. The aims of this study are to investigate whether estradiol treatment reduces inflammation in CCD841CoN, a female human colonic epithelial cell line and to uncover underlying mechanisms of estradiol effects. Methods: 17 beta-Estradiol (E2) effect was measured by Western blot after inducing inflammation of CCD841CoN by tumor necrosis factor alpha (TNF-alpha). Expression levels of estrogen receptor alpha (ER alpha) and beta (ER beta), cyclooxygenase-2 (COX-2), nuclear factor-kappa B (NF-kappa B), heme oxygenase-1 (HO-1), and NAD(P)H-quinone oxidoreductase-1 (NQO-1) were also evaluated. Results: E2 treatment induced expression of ERO but did not increase that of ER alpha. E2 treatment for 48 hours significantly elevated the expression of anti-oxidant enzymes, HO-1 and NQO-1. TNF-alpha treatment significantly increased the level of activated NF-kappa B (p < 0.05), and this increase was significantly suppressed by treatment of to nM of E2 (p < 0.05). E2 treatment ameliorated TNF-alpha-induced COX-2 expression and decrease of HO-1 expression. 4-(2-phenyl-5,7-bis(trifluoromethyl) pyrazolo(1,5-a)pyrimidin-3-yl)phenol (PHTPP), antagonist of ER beta, removed the inhibitory effect of E2 in the TNF-alpha-induced COX-2 expression (p = 0.05). Conclusions: Estrogen seems to inhibit inflammation in female human colonic epithelial cell lines, through down-regulation of NF-kappa B and COX-2 expression and induction of anti-oxidant enzymes such as HO-1 and NQO-1.

The Effects of Heat and Massage Application on Autonomic Nervous System

∙ The authors have no financial conflicts of interest. © Copyright: Yonsei University College of Medicine 2011 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial Licens

Atomic Resolution Imaging of Rotated Bilayer Graphene Sheets Using a Low kV Aberration-corrected Transmission Electron Microscope

Modern aberration-corrected transmission electron microscope (TEM) with appropriate electron beam energy is able to achieve atomic resolution imaging of single and bilayer graphene sheets. Especially, atomic configuration of bilayer graphene with a rotation angle can be identified from the direct imaging and phase reconstructed imaging since atomic resolution Moir pattern can be obtained successfully at atomic scale using an aberration-corrected TEM. This study boosts a reliable stacking order analysis, which is required for synthesized or artificially prepared multilayer graphene, and lets graphene researchers utilize the information of atomic configuration of stacked graphene layers readily.ope