Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud

Existing methods for large-scale point cloud semantic segmentation require expensive, tedious and error-prone manual point-wise annotations. Intuitively, weakly supervised training is a direct solution to reduce the cost of labeling. However, for weakly supervised large-scale point cloud semantic segmentation, too few annotations will inevitably lead to ineffective learning of network. We propose an effective weakly supervised method containing two components to solve the above problem. Firstly, we construct a pretext task, \textit{i.e.,} point cloud colorization, with a self-supervised learning to transfer the learned prior knowledge from a large amount of unlabeled point cloud to a weakly supervised network. In this way, the representation capability of the weakly supervised network can be improved by the guidance from a heterogeneous task. Besides, to generate pseudo label for unlabeled data, a sparse label propagation mechanism is proposed with the help of generated class prototypes, which is used to measure the classification confidence of unlabeled point. Our method is evaluated on large-scale point cloud datasets with different scenarios including indoor and outdoor. The experimental results show the large gain against existing weakly supervised and comparable results to fully supervised methods\footnote{Code based on mindspore: https://github.com/dmcv-ecnu/MindSpore\_ModelZoo/tree/main/WS3\_MindSpore}

Effect of High-Fat Diet on Peripheral Neuropathy in C57BL/6 Mice

Objective. Dyslipidemia may contribute to the development of peripheral neuropathy, even in prediabetics; however, few studies have evaluated vascular dysfunction and oxidative stress in patients with peripheral neuropathy. Methods. Using high-fat diet- (HFD-) induced prediabetic C57BL/6 mice, we assessed motor and sensory nerve conduction velocity (NCV) using a BIOPAC System and thermal algesia with a Plantar Test (Hargreaves’ method) Analgesia Meter. Intraepidermal nerve fiber density and mean dendrite length were tested following standard protocols. Vascular endothelial growth factor-A (VEGF-A) and 12/15-lipoxygenase (12/15-LOX) were evaluated by immunohistochemistry and Western blot, respectively. Results. HFD-fed mice showed deficits in motor and sensory NCV, thermal hyperalgesia, reduced mean dendrite length, and VEGF-A expression in the plantar skin and increased 12/15-LOX in the sciatic nerve (P<0.05 compared with controls). Conclusion. HFD may cause large myelinated nerve and small sensory nerve fiber damage, thus leading to neuropathy. The mean dendrite length may be a more sensitive marker for early detection of peripheral neuropathy. Reduced blood supply to the nerves and increased oxidative stress may contribute to the development and severity of peripheral neuropathy

Thickness of Extraocular Muscle and Orbital Fat in MRI Predicts Response to Glucocorticoid Therapy in Graves’ Ophthalmopathy

33 patients with active, moderate-severe Graves’ ophthalmopathy (GO) received 4.5 g methylprednisolone for 12 weeks and were divided by efficacy into two groups (responsive and unresponsive). All patients and 10 controls underwent orbital MRI examination at baseline. No significant difference was seen in baseline clinical characteristics between the two GO groups. The values of exophthalmos were higher in both GO groups than in the control and were higher in the responsive group versus the unresponsive group. Compared to the unresponsive group, the responsive group had a thicker inferior rectus as well as thinner orbital fat. The inferior rectus/fat ratio was significantly higher in the responsive group versus the unresponsive group. Multivariate logistic regression analysis showed that the exophthalmos value and inferior rectus/fat ratio were significantly associated with the response to glucocorticoid (GC). ROC analysis revealed that the cut-off points of the inferior rectus/fat ratio combined with the exophthalmos value to indicate efficacy were 1.42 and 20.78. For moderate-severe GO patients with CAS > 3, the combined inferior rectus/fat ratio and exophthalmos value in MRI may be a valuable indicator to predict the response to GC therapy

Scale Robust Deep Oriented-text Detection Network

Abstract(#br)Text detection is a prerequisite of text recognition, and multi-oriented text detection is a hot topic recently. The existing multi-oriented text detection methods fall short when facing two issues: 1) text scales change in a wide range, and 2) there exists the foreground-background class imbalance. In this paper, we propose a scale-robust deep multi-oriented text-detection model, which not only has the efficiency of the one-stage deep detection model, but also has the comparable accuracy of the two-stage deep text-detection model. We design the feature refining block to fuse multi-scale context features for the purpose of keeping text detection in a higher-resolution feature map. Moreover, in order to mitigate the foreground-background class imbalance, Focal Loss is adopted to up weight the hard-classified samples. Our method is implemented on four benchmark text datasets: ICDAR2013, ICDAR2015, COCO-Text and MSRA-TD500. The experimental results demonstrate that our method is superior to the existing one-stage deep text-detection models and comparable to the state-of-the-art text detection methods

Geometry-Aware Discriminative Dictionary Learning for PolSAR Image Classification

In this paper, we propose a new discriminative dictionary learning method based on Riemann geometric perception for polarimetric synthetic aperture radar (PolSAR) image classification. We made an optimization model for geometry-aware discrimination dictionary learning in which the dictionary learning (GADDL) is generalized from Euclidian space to Riemannian manifolds, and dictionary atoms are composed of manifold data. An efficient optimization algorithm based on an alternating direction multiplier method was developed to solve the model. Experiments were implemented on three public datasets: Flevoland-1989, San Francisco and Flevoland-1991. The experimental results show that the proposed method learned a discriminative dictionary with accuracies better those of comparative methods. The convergence of the model and the robustness of the initial dictionary were also verified through experiments

Online multiple instance gradient feature selection for robust visual tracking

In this paper, we focus on learning an adaptive appearance model robustly and effectively for object tracking. There are two important factors to affect object tracking, the one is how to represent the object using a discriminative appearance model, the other is how to update appearance model in an appropriate manner. In this paper, following the state-of-the-art tracking techniques which treat object tracking as a binary classification problem, we firstly employ a new gradient-based Histogram of Oriented Gradient (HOG) feature selection mechanism under Multiple Instance Learning (MIL) framework for constructing target appearance model, and then propose a novel optimization scheme to update such appearance model robustly. This is an unified framework that not only provides an efficient way of selecting the discriminative feature set which forms a powerful appearance model, but also updates appearance model in online MIL Boost manner which could achieve robust tracking overcoming the drifting problem. Experiments on several challenging video sequences demonstrate the effectiveness and robustness of our proposal. (C) 2012 Elsevier B.V. All rights reserved.National Key Technology RD Program [0101050302]; National Natural Science Foundation of China [90924026]; National Defense Basic Scientific Research program of China [B1420110155]; Specialized Research Fund for the Doctoral Program of Higher Education of China [20110121110020

Patch Proposal Network for Fast Semantic Segmentation of High-Resolution Images

Despite recent progress on the segmentation of high-resolution images, there exist an unsolved problem, i.e., the trade-off among the segmentation accuracy, memory resources and inference speed. So far, GLNet is introduced for high or ultra-resolution image segmentation, which has reduced the computational memory of the segmentation network. However, it ignores the importances of different cropped patches, and treats tiled patches equally for fusion with the whole image, resulting in high computational cost. To solve this problem, we introduce a patch proposal network (PPN) in this paper, which adaptively distinguishes the critical patches from the trivial ones to fuse with the whole image for refining segmentation. PPN is a classification network which alleviates network training burden and improves segmentation accuracy. We further embed PPN in a global-local segmentation network, instructing global branch and refinement branch to work collaboratively. We implement our method on four image datasets:DeepGlobe, ISIC, CRAG and Cityscapes, the first two are ultra-resolution image datasets and the last two are high-resolution image datasets. The experimental results show that our method achieves almost the best segmentation performance compared with the state-of-the-art segmentation methods and the inference speed is 12.9 fps on DeepGlobe and 10 fps on ISIC. Moreover, we embed PPN with the general semantic segmentation network and the experimental results on Cityscapes which contains more object classes demonstrate the generalization ability on general semantic segmentation