MMFL-Net: Multi-scale and Multi-granularity Feature Learning for Cross-domain Fashion Retrieval

Instance-level image retrieval in fashion is a challenging issue owing to its increasing importance in real-scenario visual fashion search. Cross-domain fashion retrieval aims to match the unconstrained customer images as queries for photographs provided by retailers; however, it is a difficult task due to a wide range of consumer-to-shop (C2S) domain discrepancies and also considering that clothing image is vulnerable to various non-rigid deformations. To this end, we propose a novel multi-scale and multi-granularity feature learning network (MMFL-Net), which can jointly learn global-local aggregation feature representations of clothing images in a unified framework, aiming to train a cross-domain model for C2S fashion visual similarity. First, a new semantic-spatial feature fusion part is designed to bridge the semantic-spatial gap by applying top-down and bottom-up bidirectional multi-scale feature fusion. Next, a multi-branch deep network architecture is introduced to capture global salient, part-informed, and local detailed information, and extracting robust and discrimination feature embedding by integrating the similarity learning of coarse-to-fine embedding with the multiple granularities. Finally, the improved trihard loss, center loss, and multi-task classification loss are adopted for our MMFL-Net, which can jointly optimize intra-class and inter-class distance and thus explicitly improve intra-class compactness and inter-class discriminability between its visual representations for feature learning. Furthermore, our proposed model also combines the multi-task attribute recognition and classification module with multi-label semantic attributes and product ID labels. Experimental results demonstrate that our proposed MMFL-Net achieves significant improvement over the state-of-the-art methods on the two datasets, DeepFashion-C2S and Street2Shop.Comment: 27 pages, 12 figures, Published by <Multimedia Tools and Applications

Solution-processed quasi-two-dimensional perovskite light-emitting diodes using organic small molecular electron transporting layer

In this paper, all-solution-processed LEDs using quasi-two-dimensional perovskites with organic small molecular electron transporting materials (ETMs) are successfully fabricated

Perceived acceptable uncertainty regarding comparability of endovascular treatment alone versus intravenous thrombolysis plus endovascular treatment.

BACKGROUND Most trials comparing endovascular treatment (EVT) alone versus intravenous thrombolysis with alteplase (IVT) + EVT in directly admitted patients with a stroke are non-inferiority trials. However, the margin based on the level of uncertainty regarding non-inferiority of the experimental treatment that clinicians are willing to accept to incorporate EVT alone into clinical practice remains unknown. OBJECTIVE To characterize what experienced stroke clinicians would consider an acceptable level of uncertainty for hypothetical decisions on whether to administer IVT or not before EVT in patients admitted directly to EVT-capable centers. METHODS A web-based, structured survey was distributed to a cross-section of 600 academic neurologists/neurointerventionalists. For this purpose, a response framework for a hypothetical trial comparing IVT+EVT (standard of care) with EVT alone (experimental arm) was designed. In this trial, a similar proportion of patients in each arm achieved functional independence at 90 days. Invited physicians were asked at what level of certainty they would feel comfortable skipping IVT in clinical practice, considering these hypothetical trial results. RESULTS There were 180 respondents (response rate: 30%) and 165 with complete answers. The median chosen acceptable uncertainty suggesting reasonable comparability between both treatments was an absolute difference in the rate of day 90 functional independence of 3% (mode 5%, IQR 1-5%), with higher chosen margins observed in interventionalists (aOR 2.20, 95% CI 1.06 to 4.67). CONCLUSION Physicians would generally feel comfortable skipping IVT before EVT at different certainty thresholds. Most physicians would treat with EVT alone if randomized trial data suggested that the number of patients achieving functional independence at 90 days was similar between the two groups, and one could be sufficiently sure that no more than 3 out of 100 patients would not achieve functional independence at 90 days due to skipping IVT

SymmSketch: creating symmetric 3D free-form shapes from 2D sketches

This paper presents SymmSketch — a system for creating symmetric 3D free-form shapes from 2D sketches. The reconstruction task usually separates a 3D symmetric shape into two types of shape components, that is, the self-symmetric shape component and the mutual-symmetric shape components. Each type of them can be created in an intuitive manner. According to a uniform symmetry plane, the user first draws 2D sketch lines for each shape component on a sketching plane. The z- depth information of the hand-drawn input sketches can be calculated using their property of mirror symmetry to generate 3D constructive curves. In order to provide more freedom for controlling the local geometric features of the reconstructed free- form shapes (such as their cross sections will not be limited to be traditional circular), our modeling system will create each shape component from four constructive curves. With one pair of symmetric curves and one pair of general curves, an improved cross-sectional surface blending scheme is applied to generate a parametric surface for each component. The final symmetric free- form shape will be progressively created and be represented as 3D triangular mesh. Experimental results illustrate that our system can generate symmetric complex free-form shapes effectively and conveniently

Visual saliency guided normal enhancement technique for 3D shape depiction

Visual saliency can effectively guide the viewer's visual attention to salient regions of a 3D shape. Incorporating the visual saliency measure of a polygonal mesh into the normal enhancement operation, a novel saliency guided shading scheme for shape depiction is developed in this paper. Due to the visual saliency measure of the 3D shape, our approach will adjust the illumination and shading to enhance the geometric salient features of the underlying model by dynamically perturbing the surface normals. The experimental results demonstrate that our non-photorealistic shading scheme can enhance the depiction of the underlying shape and the visual perception of its salient features for expressive rendering. Compared with previous normal enhancement techniques, our approach can effectively convey surface details to improve shape depiction without impairing the desired appearance

PGCNet: Patch Graph Convolutional Network for point cloud segmentation of indoor scenes

Semantic segmentation of 3D point clouds is a crucial task in scene understanding and is also fundamental to indoor scene applications such as indoor navigation, mobile robotics, augmented reality. Recently, deep learning frameworks have been successfully adopted to point clouds but are limited by the size of data. While most existing works focus on individual sampling points, we use surface patches as a more efficient representation and propose a novel indoor scene segmentation framework called patch graph convolution network (PGCNet). This framework treats patches as input graph nodes and subsequently aggregates neighboring node features by dynamic graph U-Net (DGU) module, which consists of dynamic edge convolution operation inside U-shaped encoder–decoder architecture. The DGU module dynamically update graph structures at each level to encode hierarchical edge features. Incorporating PGCNet, we can segment the input scene into two types, i.e., room layout and indoor objects, which is afterward utilized to carry out final rich semantic labeling of various indoor scenes. With considerable speedup training, the proposed framework achieves effective performance equivalent to state-of-the-art for segmenting standard indoor scene dataset

A coarse-to-fine point completion network with details compensation and structure enhancement

Abstract Point cloud completion, the issue of estimating the complete geometry of objects from partially-scanned point cloud data, becomes a fundamental task in many 3d vision and robotics applications. To address the limitations on inadequate prediction of shape details for traditional methods, a novel coarse-to-fine point completion network (DCSE-PCN) is introduced in this work using the modules of local details compensation and shape structure enhancement for effective geometric learning. The coarse completion stage of our network consists of two branches—a shape structure recovery branch and a local details compensation branch, which can recover the overall shape of the underlying model and the shape details of incomplete point cloud through feature learning and hierarchical feature fusion. The fine completion stage of our network employs the structure enhancement module to reinforce the correlated shape structures of the coarse repaired shape (such as regular arrangement or symmetry), thus obtaining the completed geometric shape with finer-grained details. Extensive experimental results on ShapeNet dataset and ModelNet dataset validate the effectiveness of our completion network, which can recover the shape details of the underlying point cloud whilst maintaining its overall shape. Compared to the existing methods, our coarse-to-fine completion network has shown its competitive performance on both chamfer distance (CD) and earth mover distance (EMD) errors. Such as, the repairing results on the ShapeNet dataset of our completion network are reduced by an average of $$35.62\%$$ 35.62 % , $$33.31\%$$ 33.31 % , $$29.62\%$$ 29.62 % , and $$23.62\%$$ 23.62 % in terms of CD error, comparing with PCN, FoldingNet, Atlas, and CRN methods, respectively; and also reduced by an average of $$15.63\%$$ 15.63 % , $$1.29\%$$ 1.29 % , $$64.52\%$$ 64.52 % , and $$62.87\%$$ 62.87 % in terms of EMD error, respectively. Meanwhile, the completion results on the ModelNet dataset of our network have an average reduction of $$28.41\%$$ 28.41 % , $$26.57\%$$ 26.57 % , $$20.65\%$$ 20.65 % , and $$18.55\%$$ 18.55 % in terms of CD error, comparing to PCN, FoldingNet, Atlas, and CRN methods, respectively; and also an average reduction of $$21.91\%$$ 21.91 % , $$19.59\%$$ 19.59 % , $$43.51\%$$ 43.51 % , and $$21.49\%$$ 21.49 % in terms of EMD error, respectively. Our proposed point completion network is also robust to different degrees of data incompleteness and model noise