Directional Graph Networks with Hard Weight Assignments

Miguel Dominguez, Raymond Ptucha

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Auto-TLDR; Hard Directional Graph Networks for Point Cloud Analysis

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Point cloud analysis is an important field for 3D scene understanding. It has applications in self driving cars and robotics (via LIDAR sensors), 3D graphics, and computer-aided design. Neural networks have recently achieved strong results on point cloud analysis problems such as classification and segmentation. Each point cloud network has the challenge of defining a convolution that can learn useful features on unstructured points. Some recent point cloud convolutions create separate weight matrices for separate directions like a CNN, but apply every weight matrix to every neighbor with soft assignments. This increases computational complexity and makes relatively small neighborhood aggregations expensive to compute. We propose Hard Directional Graph Networks (HDGN), a point cloud model that both learns directional weight matrices and assigns a single matrix to each neighbor, achieving directional convolutions at lower computational cost. HDGN's directional modeling achieves state-of-the-art results on multiple point cloud vision benchmarks.

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FatNet: A Feature-Attentive Network for 3D Point Cloud Processing

Chaitanya Kaul, Nick Pears, Suresh Manandhar

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Auto-TLDR; Feature-Attentive Neural Networks for Point Cloud Classification and Segmentation

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The application of deep learning to 3D point clouds is challenging due to its lack of order. Inspired by the point embeddings of PointNet and the edge embeddings of DGCNNs, we propose three improvements to the task of point cloud analysis. First, we introduce a novel feature-attentive neural network layer, a FAT layer, that combines both global point-based features and local edge-based features in order to generate better embeddings. Second, we find that applying the same attention mechanism across two different forms of feature map aggregation, max pooling and average pooling, gives better performance than either alone. Third, we observe that residual feature reuse in this setting propagates information more effectively between the layers, and makes the network easier to train. Our architecture achieves state-of-the-art results on the task of point cloud classification, as demonstrated on the ModelNet40 dataset, and an extremely competitive performance on the ShapeNet part segmentation challenge.

PC-Net: A Deep Network for 3D Point Clouds Analysis

Zhuo Chen, Tao Guan, Yawei Luo, Yuesong Wang

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Auto-TLDR; PC-Net: A Hierarchical Neural Network for 3D Point Clouds Analysis

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Due to the irregularity and sparsity of 3D point clouds, applying convolutional neural networks directly on them can be nontrivial. In this work, we propose a simple but effective approach for 3D Point Clouds analysis, named PC-Net. PC-Net directly learns on point sets and is equipped with three new operations: first, we apply a novel scale-aware neighbor search for adaptive neighborhood extracting; second, for each neighboring point, we learn a local spatial feature as a complement to their associated features; finally, at the end we use a distance re-weighted pooling to aggregate all the features from local structure. With this module, we design hierarchical neural network for point cloud understanding. For both classification and segmentation tasks, our architecture proves effective in the experiments and our models demonstrate state-of-the-art performance over existing deep learning methods on popular point cloud benchmarks.

Deep Space Probing for Point Cloud Analysis

Yirong Yang, Bin Fan, Yongcheng Liu, Hua Lin, Jiyong Zhang, Xin Liu, 蔡鑫宇 蔡鑫宇, Shiming Xiang, Chunhong Pan

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Auto-TLDR; SPCNN: Space Probing Convolutional Neural Network for Point Cloud Analysis

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3D points distribute in a continuous 3D space irregularly, thus directly adapting 2D image convolution to 3D points is not an easy job. Previous works often artificially divide the space into regular grids, yet it could be suboptimal to learn geometry. In this paper, we propose SPCNN, namely, Space Probing Convolutional Neural Network, which naturally generalizes image CNN to deal with point clouds. The key idea of SPCNN is learning to probe the 3D space in an adaptive manner. Specifically, we define a pool of learnable convolutional weights, and let each point in the local region learn to choose a suitable convolutional weight from the pool. This is achieved by constructing a geometry guided index-mapping function that implicitly establishes a correspondence between convolutional weights and some local regions in the neighborhood (Fig. 1). In this way, the index-mapping function learns to adaptively partition nearby space for local geometry pattern recognition. With this convolution as a basic operator, SPCNN, a hierarchical architecture can be developed for effective point cloud analysis. Extensive experiments on challenging benchmarks across three tasks demonstrate that SPCNN achieves the state-of-the-art or has competitive performance.

PointSpherical: Deep Shape Context for Point Cloud Learning in Spherical Coordinates

Hua Lin, Bin Fan, Yongcheng Liu, Yirong Yang, Zheng Pan, Jianbo Shi, Chunhong Pan, Huiwen Xie

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Auto-TLDR; Spherical Hierarchical Modeling of 3D Point Cloud

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We propose Spherical Hierarchical modeling of 3D point cloud. Inspired by Shape Context, we design a receptive field on each 3D point by placing a spherical coordinate on it. We sample points using the furthest point method and creating overlapping balls of points. For each ball, we divide the space into radial, polar angular and azimuthal angular bins on which we form a Spherical Hierarchy. We apply 1x1 CNN convolution on points to start the initial feature extraction. Repeated 3D CNN and max pooling over the Spherical bins propagate contextual information until all the information is condensed in the center bin. Extensive experiments on five datasets strongly evidence that our method outperform current models on various Point Cloud Learning tasks, including 2D/3D shape classification, 3D part segmentation and 3D semantic segmentation.

PS^2-Net: A Locally and Globally Aware Network for Point-Based Semantic Segmentation

Na Zhao, Tat Seng Chua, Gim Hee Lee

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Auto-TLDR; PS2-Net: A Local and Globally Aware Deep Learning Framework for Semantic Segmentation on 3D Point Clouds

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In this paper, we present the PS^2-Net - a locally and globally aware deep learning framework for semantic segmentation on 3D scene-level point clouds. In order to deeply incorporate local structures and global context to support 3D scene segmentation, our network is built on four repeatedly stacked encoders, where each encoder has two basic components: EdgeConv that captures local structures and NetVLAD that models global context. Different from existing start-of-the-art methods for point-based scene semantic segmentation that either violate or do not achieve permutation invariance, our PS2-Net is designed to be permutation invariant which is an essential property of any deep network used to process unordered point clouds. We further provide theoretical proof to guarantee the permutation invariance property of our network. We perform extensive experiments on two large-scale 3D indoor scene datasets and demonstrate that our PS2-Net is able to achieve state-of-the-art performances as compared to existing approaches.

Vehicle Classification from Profile Measures

Marco Patanè, Andrea Fusiello

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Auto-TLDR; SliceNets: Convolutional Neural Networks for 3D Object Classification of Planar Slices

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This paper proposes two novel convolutional neural networks for 3D object classification, tailored to process point clouds that are composed of planar slices (profiles). In particular, the application that we are targeting is the classification of vehicles by scanning them along planes perpendicular to the driving direction, within the context of Electronic Toll Collection. Depending on sensors configurations, the distance between slices can be measured or not, thus resulting in two types of point clouds, namely metric and non-metric. In the latter case, two coordinates are indeed metric but the third one is merely a temporal index. Our networks, named SliceNets, extract metric information from the spatial coordinates and neighborhood information from the third one (either metric or temporal), thus being able to handle both types of point clouds. Experiments on two datasets collected in the field show the effectiveness of our networks in comparison with state-of-the-art ones.

MANet: Multimodal Attention Network Based Point-View Fusion for 3D Shape Recognition

Yaxin Zhao, Jichao Jiao, Ning Li

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Auto-TLDR; Fusion Network for 3D Shape Recognition based on Multimodal Attention Mechanism

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3D shape recognition has attracted more and more attention as a task of 3D vision research. The proliferation of 3D data encourages various deep learning methods based on 3D data. Now there have been many deep learning models based on point-cloud data or multi-view data alone. However, in the era of big data, integrating data of two different modals to obtain a unified 3D shape descriptor is bound to improve the recognition accuracy. Therefore, this paper proposes a fusion network based on multimodal attention mechanism for 3D shape recognition. Considering the limitations of multi-view data, we introduce a soft attention scheme, which can use the global point-cloud features to filter the multi-view features, and then realize the effective fusion of the two features. More specifically, we obtain the enhanced multi-view features by mining the contribution of each multi-view image to the overall shape recognition, and then fuse the point-cloud features and the enhanced multi-view features to obtain a more discriminative 3D shape descriptor. We have performed relevant experiments on the ModelNet40 dataset, and experimental results verify the effectiveness of our method.

Cross-Regional Attention Network for Point Cloud Completion

Hang Wu, Yubin Miao

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Auto-TLDR; Learning-based Point Cloud Repair with Graph Convolution

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Point clouds obtained from real word scanning are always incomplete and ununiformly distributed, which would cause structural losses in 3D shape representations. Therefore, a learning-based method is introduced in this paper to repair partial point clouds and restore the complete shapes of target objects. First, we design an encoder that takes both local features and global features into consideration. Second, we establish a graph to connect the local features together, and then implement graph convolution with multi-head attention on it. The graph enables each local feature vector to search across the regions and selectively absorb other local features based on the its own features and global features. Third, we design a coarse decoder to collect cross-region features from the graph and generate coarse point clouds with low resolution, and a folding-based decoder to generate fine point clouds with high resolution. Our network is trained on six categories of objects in the ModelNet dataset, and its performance is compared with several existing methods, the results show that our network is able to generate dense complete point cloud with the highest accuracy.

Joint Supervised and Self-Supervised Learning for 3D Real World Challenges

Antonio Alliegro, Davide Boscaini, Tatiana Tommasi

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Auto-TLDR; Self-supervision for 3D Shape Classification and Segmentation in Point Clouds

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Point cloud processing and 3D shape understanding are very challenging tasks for which deep learning techniques have demonstrated great potentials. Still further progresses are essential to allow artificial intelligent agents to interact with the real world. In many practical conditions the amount of annotated data may be limited and integrating new sources of knowledge becomes crucial to support autonomous learning. Here we consider several scenarios involving synthetic and real world point clouds where supervised learning fails due to data scarcity and large domain gaps. We propose to enrich standard feature representations by leveraging self-supervision through a multi-task model that can solve a 3D puzzle while learning the main task of shape classification or part segmentation. An extensive analysis investigating few-shot, transfer learning and cross-domain settings shows the effectiveness of our approach with state-of-the-art results for 3D shape classification and part segmentation.

Progressive Scene Segmentation Based on Self-Attention Mechanism

Yunyi Pan, Yuan Gan, Kun Liu, Yan Zhang

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Auto-TLDR; Two-Stage Semantic Scene Segmentation with Self-Attention

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Semantic scene segmentation is vital for a large variety of applications as it enables understanding of 3D data. Nowadays, various approaches based upon point clouds ignore the mathematical distribution of points and treat the points equally. The methods following this direction neglect the imbalance problem of samples that naturally exists in scenes. To avoid these issues, we propose a two-stage semantic scene segmentation framework based on self-attention mechanism and achieved state-of-the-art performance on 3D scene understanding tasks. We split the whole task into two small ones which efficiently relief the sample imbalance issue. In addition, we have designed a new self-attention block which could be inserted into submanifold convolution networks to model the long-range dependencies that exists among points. The proposed network consists of an encoder and a decoder, with the spatial-wise and channel-wise attention modules inserted. The two-stage network shares a U-Net architecture and is an end-to-end trainable framework which could predict the semantic label for the scene point clouds fed into it. Experiments on standard benchmarks of 3D scenes implies that our network could perform at par or better than the existing state-of-the-art methods.

3D Facial Matching by Spiral Convolutional Metric Learning and a Biometric Fusion-Net of Demographic Properties

Soha Sadat Mahdi, Nele Nauwelaers, Philip Joris, Giorgos Bouritsas, Imperial London, Sergiy Bokhnyak, Susan Walsh, Mark Shriver, Michael Bronstein, Peter Claes

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Auto-TLDR; Multi-biometric Fusion for Biometric Verification using 3D Facial Mesures

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Face recognition is a widely accepted biometric verification tool, as the face contains a lot of information about the identity of a person. In this study, a 2-step neural-based pipeline is presented for matching 3D facial shape to multiple DNA-related properties (sex, age, BMI and genomic background). The first step consists of a triplet loss-based metric learner that compresses facial shape into a lower dimensional embedding while preserving information about the property of interest. Most studies in the field of metric learning have only focused on Euclidean data. In this work, geometric deep learning is employed to learn directly from 3D facial meshes. To this end, spiral convolutions are used along with a novel mesh-sampling scheme that retains uniformly sampled 3D points at different levels of resolution. The second step is a multi-biometric fusion by a fully connected neural network. The network takes an ensemble of embeddings and property labels as input and returns genuine and imposter scores. Since embeddings are accepted as an input, there is no need to train classifiers for the different properties and available data can be used more efficiently. Results obtained by a 10-fold cross-validation for biometric verification show that combining multiple properties leads to stronger biometric systems. Furthermore, the proposed neural-based pipeline outperforms a linear baseline, which consists of principal component analysis, followed by classification with linear support vector machines and a Naïve Bayes-based score-fuser.

Self-Supervised Detection and Pose Estimation of Logistical Objects in 3D Sensor Data

Nikolas Müller, Jonas Stenzel, Jian-Jia Chen

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Auto-TLDR; A self-supervised and fully automated deep learning approach for object pose estimation using simulated 3D data

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Localization of objects in cluttered scenes with machine learning methods is a fairly young research area. Despite the high potential of object localization for full process automation in Industry 4.0 and logistical environments, 3D data sets for such applications to train machine learning models are not openly available and less publications have been made on that topic. To the authors knowledge, this is the first publication that describes a self-supervised and fully automated deep learning approach for object pose estimation using simulated 3D data. The solution covers the simulated generation of training data, the detection of objects in point clouds using a fully convolutional feedforward network and the computation of the pose for each detected object instance.

GCNs-Based Context-Aware Short Text Similarity Model

Xiaoqi Sun

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Auto-TLDR; Context-Aware Graph Convolutional Network for Text Similarity

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Semantic textual similarity is a fundamental task in text mining and natural language processing (NLP), which has profound research value. The essential step for text similarity is text representation learning. Recently, researches have explored the graph convolutional network (GCN) techniques on text representation, since GCN does well in handling complex structures and preserving syntactic information. However, current GCN models are usually limited to very shallow layers due to the vanishing gradient problem, which cannot capture non-local dependency information of sentences. In this paper, we propose a GCNs-based context-aware (GCSTS) model that applies iterated GCN blocks to train deeper GCNs. Recurrently employing the same GCN block prevents over-fitting and provides broad effective input width. Combined with dense connections, GCSTS can be trained more deeply. Besides, we use dynamic graph structures in the block, which further extend the receptive field of each vertex in graphs, learning better sentence representations. Experiments show that our model outperforms existing models on several text similarity datasets, while also verify that GCNs-based text representation models can be trained in a deeper manner, rather than being trained in two or three layers.

Learning Interpretable Representation for 3D Point Clouds

Feng-Guang Su, Ci-Siang Lin, Yu-Chiang Frank Wang

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Auto-TLDR; Disentangling Body-type and Pose Information from 3D Point Clouds Using Adversarial Learning

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Point clouds have emerged as a popular representation of 3D visual data. With a set of unordered 3D points, one typically needs to transform them into latent representation before further classification and segmentation tasks. However, one cannot easily interpret such encoded latent representation. To address this issue, we propose a unique deep learning framework for disentangling body-type and pose information from 3D point clouds. Extending from autoenoder, we advance adversarial learning a selected feature type, while classification and data recovery can be additionally observed. Our experiments confirm that our model can be successfully applied to perform a wide range of 3D applications like shape synthesis, action translation, shape/action interpolation, and synchronization.

Ghost Target Detection in 3D Radar Data Using Point Cloud Based Deep Neural Network

Mahdi Chamseddine, Jason Rambach, Oliver Wasenmüler, Didier Stricker

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Auto-TLDR; Point Based Deep Learning for Ghost Target Detection in 3D Radar Point Clouds

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Ghost targets are targets that appear at wrong locations in radar data and are caused by the presence of multiple indirect reflections between the target and the sensor. In this work, we introduce the first point based deep learning approach for ghost target detection in 3D radar point clouds. This is done by extending the PointNet network architecture by modifying its input to include radar point features beyond location and introducing skip connetions. We compare different input modalities and analyze the effects of the changes we introduced. We also propose an approach for automatic labeling of ghost targets 3D radar data using lidar as reference. The algorithm is trained and tested on real data in various driving scenarios and the tests show promising results in classifying real and ghost radar targets.

3D Point Cloud Registration Based on Cascaded Mutual Information Attention Network

Xiang Pan, Xiaoyi Ji

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Auto-TLDR; Cascaded Mutual Information Attention Network for 3D Point Cloud Registration

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For 3D point cloud registration, how to improve the local feature correlation of two point clouds is a challenging problem. In this paper, we propose a cascaded mutual information attention registration network. The network improves the accuracy of point cloud registration by stacking residual structure and using lateral connection. Firstly, the local reference coordinate system is defined by spherical representation for the local point set, which improves the stability and reliability of local features under noise. Secondly, the attention structure is used to improve the network depth and ensure the convergence of the network. Furthermore, a lateral connection is introduced into the network to avoid the loss of features in the process of concatenation. In the experimental part, the results of different algorithms are compared. It can be found that the proposed cascaded network can enhance the correlation of local features between different point clouds. As a result, it improves the registration accuracy significantly over the DCP and other typical algorithms.

3D Semantic Labeling of Photogrammetry Meshes Based on Active Learning

Mengqi Rong, Shuhan Shen, Zhanyi Hu

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Auto-TLDR; 3D Semantic Expression of Urban Scenes Based on Active Learning

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As different urban scenes are similar but still not completely consistent, coupled with the complexity of labeling directly in 3D, high-level understanding of 3D scenes has always been a tricky problem. In this paper, we propose a procedural approach for 3D semantic expression of urban scenes based on active learning. We first start with a small labeled image set to fine-tune a semantic segmentation network and then project its probability map onto a 3D mesh model for fusion, finally outputs a 3D semantic mesh model in which each facet has a semantic label and a heat model showing each facet’s confidence. Our key observation is that our algorithm is iterative, in each iteration, we use the output semantic model as a supervision to select several valuable images for annotation to co-participate in the fine-tuning for overall improvement. In this way, we reduce the workload of labeling but not the quality of 3D semantic model. Using urban areas from two different cities, we show the potential of our method and demonstrate its effectiveness.

Fast and Accurate Real-Time Semantic Segmentation with Dilated Asymmetric Convolutions

Leonel Rosas-Arias, Gibran Benitez-Garcia, Jose Portillo-Portillo, Gabriel Sanchez-Perez, Keiji Yanai

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Auto-TLDR; FASSD-Net: Dilated Asymmetric Pyramidal Fusion for Real-Time Semantic Segmentation

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Recent works have shown promising results applied to real-time semantic segmentation tasks. To maintain fast inference speed, most of the existing networks make use of light decoders, or they simply do not use them at all. This strategy helps to maintain a fast inference speed; however, their accuracy performance is significantly lower in comparison to non-real-time semantic segmentation networks. In this paper, we introduce two key modules aimed to design a high-performance decoder for real-time semantic segmentation for reducing the accuracy gap between real-time and non-real-time segmentation networks. Our first module, Dilated Asymmetric Pyramidal Fusion (DAPF), is designed to substantially increase the receptive field on the top of the last stage of the encoder, obtaining richer contextual features. Our second module, Multi-resolution Dilated Asymmetric (MDA) module, fuses and refines detail and contextual information from multi-scale feature maps coming from early and deeper stages of the network. Both modules exploit contextual information without excessively increasing the computational complexity by using asymmetric convolutions. Our proposed network entitled “FASSD-Net” reaches 78.8% of mIoU accuracy on the Cityscapes validation dataset at 41.1 FPS on full resolution images (1024x2048). Besides, with a light version of our network, we reach 74.1% of mIoU at 133.1 FPS (full resolution) on a single NVIDIA GTX 1080Ti card with no additional acceleration techniques. The source code and pre-trained models are available at https://github.com/GibranBenitez/FASSD-Net.

Kernel-based Graph Convolutional Networks

Hichem Sahbi

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Auto-TLDR; Spatial Graph Convolutional Networks in Recurrent Kernel Hilbert Space

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Learning graph convolutional networks (GCNs) is an emerging field which aims at generalizing deep learning to arbitrary non-regular domains. Most of the existing GCNs follow a neighborhood aggregation scheme, where the representation of a node is recursively obtained by aggregating its neighboring node representations using averaging or sorting operations. However, these operations are either ill-posed or weak to be discriminant or increase the number of training parameters and thereby the computational complexity and the risk of overfitting. In this paper, we introduce a novel GCN framework that achieves spatial graph convolution in a reproducing kernel Hilbert space. The latter makes it possible to design, via implicit kernel representations, convolutional graph filters in a high dimensional and more discriminating space without increasing the number of training parameters. The particularity of our GCN model also resides in its ability to achieve convolutions without explicitly realigning nodes in the receptive fields of the learned graph filters with those of the input graphs, thereby making convolutions permutation agnostic and well defined. Experiments conducted on the challenging task of skeleton-based action recognition show the superiority of the proposed method against different baselines as well as the related work.

Boundary-Aware Graph Convolution for Semantic Segmentation

Hanzhe Hu, Jinshi Cui, Jinshi Hongbin Zha

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Auto-TLDR; Boundary-Aware Graph Convolution for Semantic Segmentation

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Recent works have made great progress in semantic segmentation by exploiting contextual information in a local or global manner with dilated convolutions, pyramid pooling or self-attention mechanism. However, few works have focused on harvesting boundary information to improve the segmentation performance. In order to enhance the feature similarity within the object and keep discrimination from other objects, we propose a boundary-aware graph convolution (BGC) module to propagate features within the object. The graph reasoning is performed among pixels of the same object apart from the boundary pixels. Based on the proposed BGC module, we further introduce the Boundary-aware Graph Convolution Network(BGCNet), which consists of two main components including a basic segmentation network and the BGC module, forming a coarse-to-fine paradigm. Specifically, the BGC module takes the coarse segmentation feature map as node features and boundary prediction to guide graph construction. After graph convolution, the reasoned feature and the input feature are fused together to get the refined feature, producing the refined segmentation result. We conduct extensive experiments on three popular semantic segmentation benchmarks including Cityscapes, PASCAL VOC 2012 and COCO Stuff, and achieve state-of-the-art performance on all three benchmarks.

EdgeNet: Semantic Scene Completion from a Single RGB-D Image

Aloisio Dourado, Teofilo De Campos, Adrian Hilton, Hansung Kim

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Auto-TLDR; Semantic Scene Completion using 3D Depth and RGB Information

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Semantic scene completion is the task of predicting a complete 3D representation of volumetric occupancy with corresponding semantic labels for a scene from a single point of view. In this paper, we present EdgeNet, a new end-to-end neural network architecture that fuses information from depth and RGB, explicitly representing RGB edges in 3D space. Previous works on this task used either depth-only or depth with colour by projecting 2D semantic labels generated by a 2D segmentation network into the 3D volume, requiring a two step training process. Our EdgeNet representation encodes colour information in 3D space using edge detection and flipped truncated signed distance, which improves semantic completion scores especially in hard to detect classes. We achieved state-of-the-art scores on both synthetic and real datasets with a simpler and a more computationally efficient training pipeline than competing approaches.

ESResNet: Environmental Sound Classification Based on Visual Domain Models

Andrey Guzhov, Federico Raue, Jörn Hees, Andreas Dengel

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Auto-TLDR; Environmental Sound Classification with Short-Time Fourier Transform Spectrograms

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Environmental Sound Classification (ESC) is an active research area in the audio domain and has seen a lot of progress in the past years. However, many of the existing approaches achieve high accuracy by relying on domain-specific features and architectures, making it harder to benefit from advances in other fields (e.g., the image domain). Additionally, some of the past successes have been attributed to a discrepancy of how results are evaluated (i.e., on unofficial splits of the UrbanSound8K (US8K) dataset), distorting the overall progression of the field. The contribution of this paper is twofold. First, we present a model that is inherently compatible with mono and stereo sound inputs. Our model is based on simple log-power Short-Time Fourier Transform (STFT) spectrograms and combines them with several well-known approaches from the image domain (i.e., ResNet, Siamese-like networks and attention). We investigate the influence of cross-domain pre-training, architectural changes, and evaluate our model on standard datasets. We find that our model out-performs all previously known approaches in a fair comparison by achieving accuracies of 97.0 % (ESC-10), 91.5 % (ESC-50) and 84.2 % / 85.4 % (US8K mono / stereo). Second, we provide a comprehensive overview of the actual state of the field, by differentiating several previously reported results on the US8K dataset between official or unofficial splits. For better reproducibility, our code (including any re-implementations) is made available.

Hybrid Approach for 3D Head Reconstruction: Using Neural Networks and Visual Geometry

Oussema Bouafif, Bogdan Khomutenko, Mohammed Daoudi

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Auto-TLDR; Recovering 3D Head Geometry from a Single Image using Deep Learning and Geometric Techniques

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Recovering the 3D geometric structure of a face from a single input image is a challenging active research area in computer vision. In this paper, we present a novel method for reconstructing 3D heads from a single or multiple image(s) using a hybrid approach based on deep learning and geometric techniques. We propose an encoder-decoder network based on the U-net architecture and trained on synthetic data only. It predicts both pixel-wise normal vectors and landmarks maps from a single input photo. Landmarks are used for the pose computation and the initialization of the optimization problem, which, in turn, reconstructs the 3D head geometry by using a parametric morphable model and normal vector fields. State-of-the-art results are achieved through qualitative and quantitative evaluation tests on both single and multi-view settings. Despite the fact that the model was trained only on synthetic data, it successfully recovers 3D geometry and precise poses for real-world images.

A General Model for Learning Node and Graph Representations Jointly

Chaofan Chen

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Auto-TLDR; Joint Community Detection/Dynamic Routing for Graph Classification

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This paper focuses on two fundamental graph recognition tasks: node classification and graph classification. Existing methods usually learn the node and graph representations for these two tasks separately, and ignore modeling the relations between the local and global structures. In this paper, we propose a general approach to learn the local and global features collaboratively: (1) in order to characterize the correlation among nodes and communities (a set of nodes), we employ the joint community detection/dynamic routing modules to generate the clustering assignment matrices at first and then utilize these matrices to cluster nodes to capture the global information of graphs (locally relevant graph representations). Inspired by the success of spectral clustering, we minimize the ratiocut loss to help optimize the learned assignment matrices. (2) We maximize the mutual information between local and global representations to help learn the globally relevant node representations. Experimental results on a variety of node and graph classification benchmarks show that our model can achieve superior performance over the state-of-the-art approaches.

Region and Relations Based Multi Attention Network for Graph Classification

Manasvi Aggarwal, M. Narasimha Murty

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Auto-TLDR; R2POOL: A Graph Pooling Layer for Non-euclidean Structures

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Graphs are non-euclidean structures that can represent many relational data efficiently. Many studies have proposed the convolution and the pooling operators on the non-euclidean domain. The graph convolution operators have shown astounding performance on various tasks such as node representation and classification. For graph classification, different pooling techniques are introduced, but none of them has considered both neighborhood of the node and the long-range dependencies of the node. In this paper, we propose a novel graph pooling layer R2POOL, which balances the structure information around the node as well as the dependencies with far away nodes. Further, we propose a new training strategy to learn coarse to fine representations. We add supervision at only intermediate levels to generate predictions using only intermediate-level features. For this, we propose the concept of an alignment score. Moreover, each layer's prediction is controlled by our proposed branch training strategy. This complete training helps in learning dominant class features at each layer for representing graphs. We call the combined model by R2MAN. Experiments show that R2MAN the potential to improve the performance of graph classification on various datasets.

Distinctive 3D Local Deep Descriptors

Fabio Poiesi, Davide Boscaini

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Auto-TLDR; DIPs: Local Deep Descriptors for Point Cloud Regression

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We present a simple but yet effective method for learning distinctive 3D local deep descriptors (DIPs) that can be used to register point clouds without requiring an initial alignment. Point cloud patches are extracted, canonicalised with respect to their estimated local reference frame and encoded into rotation-invariant compact descriptors by a PointNet-based deep neural network. DIPs can effectively generalise across different sensor modalities because they are learnt end-to-end from locally and randomly sampled points. Moreover, because DIPs encode only local geometric information, they are robust to clutter, occlusions and missing regions. We evaluate and compare DIPs against alternative hand-crafted and deep descriptors on several indoor and outdoor datasets reconstructed using different sensors. Results show that DIPs (i) achieve comparable results to the state-of-the-art on RGB-D indoor scenes (3DMatch dataset), (ii) outperform state-of-the-art by a large margin on laser-scanner outdoor scenes (ETH dataset), and (iii) generalise to indoor scenes reconstructed with the Visual-SLAM system of Android ARCore.

PointDrop: Improving Object Detection from Sparse Point Clouds Via Adversarial Data Augmentation

Wenxin Ma, Jian Chen, Qing Du, Wei Jia

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Auto-TLDR; PointDrop: Improving Robust 3D Object Detection to Sparse Point Clouds

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Current 3D object detection methods achieve accurate and efficient results on the standard point cloud dataset. However, in real-world applications, due to the expensive cost of obtaining the annotated 3D object detection data, we expect to directly apply the model trained on the standard dataset to real-world scenarios. This strategy may fail because the point cloud samples obtained in the real-world scenarios may be much sparser due to various reasons (occlusion, low reflectivity of objects and fewer laser beams) and existing methods do not consider the limitations of their models on sparse point clouds. To improve the robustness of an object detector to sparser point clouds, we propose PointDrop, which learns to drop the features of some key points in the point clouds to generate challenging sparse samples for data augmentation. Moreover, PointDrop is able to adjust the difficulty of the generated samples based on the capacity of the detector and thus progressively improve the performance of the detector. We create two sparse point clouds datasets from the KITTI dataset to evaluate our method, and the experimental results show that PointDrop significantly improves the robustness of the detector to sparse point clouds.

Global-Local Attention Network for Semantic Segmentation in Aerial Images

Minglong Li, Lianlei Shan, Weiqiang Wang

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Auto-TLDR; GLANet: Global-Local Attention Network for Semantic Segmentation

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Errors in semantic segmentation task could be classified into two types: large area misclassification and local inaccurate boundaries. Previously attention based methods capture rich global contextual information, this is beneficial to diminish the first type of error, but local imprecision still exists. In this paper we propose Global-Local Attention Network (GLANet) with a simultaneous consideration of global context and local details. Specifically, our GLANet is composed of two branches namely global attention branch and local attention branch, and three different modules are embedded in the two branches for the purpose of modeling semantic interdependencies in spatial, channel and boundary dimensions respectively. We sum the outputs of the two branches to further improve feature representation, leading to more precise segmentation results. The proposed method achieves very competitive segmentation accuracy on two public aerial image datasets, bringing significant improvements over baseline.

Enhancing Deep Semantic Segmentation of RGB-D Data with Entangled Forests

Matteo Terreran, Elia Bonetto, Stefano Ghidoni

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Auto-TLDR; FuseNet: A Lighter Deep Learning Model for Semantic Segmentation

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Semantic segmentation is a problem which is getting more and more attention in the computer vision community. Nowadays, deep learning methods represent the state of the art to solve this problem, and the trend is to use deeper networks to get higher performance. The drawback with such models is a higher computational cost, which makes it difficult to integrate them on mobile robot platforms. In this work we want to explore how to obtain lighter deep learning models without compromising performance. To do so we will consider the features used in the Entangled Random Forest algorithm and we will study the best strategies to integrate these within FuseNet deep network. Such new features allow us to shrink the network size without loosing performance, obtaining hence a lighter model which achieves state-of-the-art performance on the semantic segmentation task and represents an interesting alternative for mobile robotics applications, where computational power and energy are limited.

Joint Semantic-Instance Segmentation of 3D Point Clouds: Instance Separation and Semantic Fusion

Min Zhong, Gang Zeng

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Auto-TLDR; Joint Semantic Segmentation and Instance Separation of 3D Point Clouds

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This paper introduces an approach for jointly addressing semantic segmentation (SS) and instance segmentation (IS) of 3D point clouds. Two novel modules are designed to model the interplay between SS and IS. Specifically, we develop an Instance Separation Module that supplements the position-invariance semantic feature with the instance-specific centroid position to help separate different instances. To fuse the semantic information within a single instance, an attention-based Semantic Fusion Module is proposed to encode attention maps in the instance embedding space, which are applied to fuse semantic information in the semantic feature space. The proposed method is thoroughly evaluated on the S3DIS dataset. Compared with the excellent method ASIS, our approach achieves significant improvements across all evaluation metrics in both IS and SS.

MixedFusion: 6D Object Pose Estimation from Decoupled RGB-Depth Features

Hangtao Feng, Lu Zhang, Xu Yang, Zhiyong Liu

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Auto-TLDR; MixedFusion: Combining Color and Point Clouds for 6D Pose Estimation

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Estimating the 6D pose of objects is an important process for intelligent systems to achieve interaction with the real-world. As the RGB-D sensors become more accessible, the fusion-based methods have prevailed, since the point clouds provide complementary geometric information with RGB values. However, Due to the difference in feature space between color image and depth image, the network structures that directly perform point-to-point matching fusion do not effectively fuse the features of the two. In this paper, we propose a simple but effective approach, named MixedFusion. Different from the prior works, we argue that the spatial correspondence of color and point clouds could be decoupled and reconnected, thus enabling a more flexible fusion scheme. By performing the proposed method, more informative points can be mixed and fused with rich color features. Extensive experiments are conducted on the challenging LineMod and YCB-Video datasets, show that our method significantly boosts the performance without introducing extra overheads. Furthermore, when the minimum tolerance of metric narrows, the proposed approach performs better for the high-precision demands.

A New Geodesic-Based Feature for Characterization of 3D Shapes: Application to Soft Tissue Organ Temporal Deformations

Karim Makki, Amine Bohi, Augustin Ogier, Marc-Emmanuel Bellemare

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Auto-TLDR; Spatio-Temporal Feature Descriptors for 3D Shape Characterization from Point Clouds

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Spatio-temporal feature descriptors are of great importance for characterizing the local changes of 3D deformable shapes. In this study, we propose a method for characterizing 3D shapes from point clouds and we show a direct application on a study of organ temporal deformations. As an example, we characterize the behavior of the bladder during forced respiratory motion with a reduced number of 3D surface points: first, a set of equidistant points representing the vertices of quadrilateral mesh for the organ surface are tracked throughout a long dynamic MRI sequence using a large deformation diffeomorphic metric mapping (LDDMM) framework. Second, a novel 3D shape descriptor invariant to translation, scale and rotation is proposed for characterizing the temporal organ deformations by employing an Eulerian Partial Differential Equations (PDEs) methodology. We demonstrate the robustness of our feature on both synthetic 3D shapes and realistic dynamic Magnetic Resonance Imaging (MRI) data sequences portraying the bladder deformation during a forced breathing exercise. Promising results are obtained, showing that the proposed feature may be useful for several computer vision applications such as medical imaging, aerodynamics and robotics.

Aggregating Object Features Based on Attention Weights for Fine-Grained Image Retrieval

Hongli Lin, Yongqi Song, Zixuan Zeng, Weisheng Wang

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Auto-TLDR; DSAW: Unsupervised Dual-selection for Fine-Grained Image Retrieval

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Object localization and local feature representation are key issues in fine-grained image retrieval. However, the existing unsupervised methods still need to be improved in these two aspects. For conquering these issues in a unified framework, a novel unsupervised scheme, named DSAW for short, is presented in this paper. Firstly, we proposed a dual-selection (DS) method, which achieves more accurate object localization by using adaptive threshold method to perform feature selection on local and global activation map in turn. Secondly, a novel and faster self-attention weights (AW) method is developed to weight local features by measuring their importance in the global context. Finally, we also evaluated the performance of the proposed method on five fine-grained image datasets and the results showed that our DSAW outperformed the existing best method.

Human Segmentation with Dynamic LiDAR Data

Tao Zhong, Wonjik Kim, Masayuki Tanaka, Masatoshi Okutomi

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Auto-TLDR; Spatiotemporal Neural Network for Human Segmentation with Dynamic Point Clouds

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Consecutive LiDAR scans and depth images compose dynamic 3D sequences, which contain more abundant spatiotemporal information than a single frame. Similar to the development history of image and video perception, dynamic 3D sequence perception starts to come into sight after inspiring research on static 3D data perception. This work proposes a spatiotemporal neural network for human segmentation with the dynamic LiDAR point clouds. It takes a sequence of depth images as input. It has a two-branch structure, i.e., the spatial segmentation branch and the temporal velocity estimation branch. The velocity estimation branch is designed to capture motion cues from the input sequence and then propagates them to the other branch. So that the segmentation branch segments humans according to both spatial and temporal features. These two branches are jointly learned on a generated dynamic point cloud data set for human recognition. Our works fill in the blank of dynamic point cloud perception with the spherical representation of point cloud and achieves high accuracy. The experiments indicate that the introduction of temporal feature benefits the segmentation of dynamic point cloud perception.

Learning Connectivity with Graph Convolutional Networks

Hichem Sahbi

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Auto-TLDR; Learning Graph Convolutional Networks Using Topological Properties of Graphs

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Learning graph convolutional networks (GCNs) is an emerging field which aims at generalizing convolutional operations to arbitrary non-regular domains. In particular, GCNs operating on spatial domains show superior performances compared to spectral ones, however their success is highly dependent on how the topology of input graphs is defined. In this paper, we introduce a novel framework for graph convolutional networks that learns the topological properties of graphs. The design principle of our method is based on the optimization of a constrained objective function which learns not only the usual convolutional parameters in GCNs but also a transformation basis that conveys the most relevant topological relationships in these graphs. Experiments conducted on the challenging task of skeleton-based action recognition shows the superiority of the proposed method compared to handcrafted graph design as well as the related work.

Domain Siamese CNNs for Sparse Multispectral Disparity Estimation

David-Alexandre Beaupre, Guillaume-Alexandre Bilodeau

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Auto-TLDR; Multispectral Disparity Estimation between Thermal and Visible Images using Deep Neural Networks

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Multispectral disparity estimation is a difficult task for many reasons: it as all the same challenges as traditional visible-visible disparity estimation (occlusions, repetitive patterns, textureless surfaces), in addition of having very few common visual information between images (e.g. color information vs. thermal information). In this paper, we propose a new CNN architecture able to do disparity estimation between images from different spectrum, namely thermal and visible in our case. Our proposed model takes two patches as input and proceeds to do domain feature extraction for each of them. Features from both domains are then merged with two fusion operations, namely correlation and concatenation. These merged vectors are then forwarded to their respective classification heads, which are responsible for classifying the inputs as being same or not. Using two merging operations gives more robustness to our feature extraction process, which leads to more precise disparity estimation. Our method was tested using the publicly available LITIV 2014 and LITIV 2018 datasets, and showed best results when compared to other state of the art methods.

TreeRNN: Topology-Preserving Deep Graph Embedding and Learning

Yecheng Lyu, Ming Li, Xinming Huang, Ulkuhan Guler, Patrick Schaumont, Ziming Zhang

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Auto-TLDR; TreeRNN: Recurrent Neural Network for General Graph Classification

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General graphs are difficult for learning due to their irregular structures. Existing works employ message passing along graph edges to extract local patterns using customized graph kernels, but few of them are effective for the integration of such local patterns into global features. In contrast, in this paper we study the methods to transfer the graphs into trees so that explicit orders are learned to direct the feature integration from local to global. To this end, we apply the breadth first search (BFS) to construct trees from the graphs, which adds direction to the graph edges from the center node to the peripheral nodes. In addition, we proposed a novel projection scheme that transfer the trees to image representations, which is suitable for conventional convolution neural networks (CNNs) and recurrent neural networks (RNNs). To best learn the patterns from the graph-tree-images, we propose TreeRNN, a 2D RNN architecture that recurrently integrates the image pixels by rows and columns to help classify the graph categories. We evaluate the proposed method on several graph classification datasets, and manage to demonstrate comparable accuracy with the state-of-the-art on MUTAG, PTC-MR and NCI1 datasets.

Self-Supervised Learning with Graph Neural Networks for Region of Interest Retrieval in Histopathology

Yigit Ozen, Selim Aksoy, Kemal Kosemehmetoglu, Sevgen Onder, Aysegul Uner

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Auto-TLDR; Self-supervised Contrastive Learning for Deep Representation Learning of Histopathology Images

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Deep learning has achieved successful performance in representation learning and content-based retrieval of histopathology images. The commonly used setting in deep learning-based approaches is supervised training of deep neural networks for classification, and using the trained model to extract representations that are used for computing and ranking the distances between images. However, there are two remaining major challenges. First, supervised training of deep neural networks requires large amount of manually labeled data which is often limited in the medical field. Transfer learning has been used to overcome this challenge, but its success remained limited. Second, the clinical practice in histopathology necessitates working with regions of interest (ROI) of multiple diagnostic classes with arbitrary shapes and sizes. The typical solution to this problem is to aggregate the representations of fixed-sized patches cropped from these regions to obtain region-level representations. However, naive methods cannot sufficiently exploit the rich contextual information in the complex tissue structures. To tackle these two challenges, we propose a generic method that utilizes graph neural networks (GNN), combined with a self-supervised training method using a contrastive loss. GNN enables representing arbitrarily-shaped ROIs as graphs and encoding contextual information. Self-supervised contrastive learning improves quality of learned representations without requiring labeled data. The experiments using a challenging breast histopathology data set show that the proposed method achieves better performance than the state-of-the-art.

MEG: Multi-Evidence GNN for Multimodal Semantic Forensics

Ekraam Sabir, Ayush Jaiswal, Wael Abdalmageed, Prem Natarajan

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Auto-TLDR; Scalable Image Repurposing Detection with Graph Neural Network Based Model

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Image repurposing is a category of fake news where a digitally unmanipulated image is misrepresented by means of its accompanying metadata such as captions, location, etc., where the image and accompanying metadata together comprise a multimedia package. The problem setup is to authenticate a query multimedia package using a reference dataset of potentially related packages as evidences. Existing methods are limited to using a single evidence (retrieved package), which ignores potential performance improvement from the use of multiple evidences. In this work, we introduce a novel graph neural network based model for image repurposing detection, which effectively utilizes multiple retrieved packages as evidences and is scalable with the number of evidences. We compare the scalability and performance of our model against existing methods. Experimental results show that the proposed model outperforms existing state-of-the-art for image repurposing detection with an error reduction of up to 25%.

Norm Loss: An Efficient yet Effective Regularization Method for Deep Neural Networks

Theodoros Georgiou, Sebastian Schmitt, Thomas Baeck, Wei Chen, Michael Lew

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Auto-TLDR; Weight Soft-Regularization with Oblique Manifold for Convolutional Neural Network Training

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Convolutional neural network training can suffer from diverse issues like exploding or vanishing gradients, scaling-based weight space symmetry and covariant-shift. In order to address these issues, researchers develop weight regularization methods and activation normalization methods. In this work we propose a weight soft-regularization method based on the Oblique manifold. The proposed method uses a loss function which pushes each weight vector to have a norm close to one, i.e. the weight matrix is smoothly steered toward the so-called Oblique manifold. We evaluate our method on the very popular CIFAR-10, CIFAR-100 and ImageNet 2012 datasets using two state-of-the-art architectures, namely the ResNet and wide-ResNet. Our method introduces negligible computational overhead and the results show that it is competitive to the state-of-the-art and in some cases superior to it. Additionally, the results are less sensitive to hyperparameter settings such as batch size and regularization factor.

ResNet-Like Architecture with Low Hardware Requirements

Elena Limonova, Daniil Alfonso, Dmitry Nikolaev, Vladimir V. Arlazarov

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Auto-TLDR; BM-ResNet: Bipolar Morphological ResNet for Image Classification

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One of the most computationally intensive parts in modern recognition systems is an inference of deep neural networks that are used for image classification, segmentation, enhancement, and recognition. The growing popularity of edge computing makes us look for ways to reduce its time for mobile and embedded devices. One way to decrease the neural network inference time is to modify a neuron model to make it more efficient for computations on a specific device. The example of such a model is a bipolar morphological neuron model. The bipolar morphological neuron is based on the idea of replacing multiplication with addition and maximum operations. This model has been demonstrated for simple image classification with LeNet-like architectures [1]. In the paper, we introduce a bipolar morphological ResNet (BM-ResNet) model obtained from a much more complex ResNet architecture by converting its layers to bipolar morphological ones. We apply BM-ResNet to image classification on MNIST and CIFAR-10 datasets with only a moderate accuracy decrease from 99.3% to 99.1% and from 85.3% to 85.1%. We also estimate the computational complexity of the resulting model. We show that for the majority of ResNet layers, the considered model requires 2.1-2.9 times fewer logic gates for implementation and 15-30% lower latency.

On the Global Self-attention Mechanism for Graph Convolutional Networks

Chen Wang, Deng Chengyuan

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Auto-TLDR; Global Self-Attention Mechanism for Graph Convolutional Networks

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Applying Global Self-Attention (GSA) mechanism over features has achieved remarkable success on Convolutional Neural Networks (CNNs). However, it is not clear if Graph Convolutional Networks (GCNs) can similarly benefit from such a technique. In this paper, inspired by the similarity between CNNs and GCNs, we study the impact of the Global Self-Attention mechanism on GCNs. We find that consistent with the intuition, the GSA mechanism allows GCNs to capture feature-based vertex relations regardless of edge connections; As a result, the GSA mechanism can introduce extra expressive power to the GCNs. Furthermore, we analyze the impacts of the GSA mechanism on the issues of overfitting and over-smoothing. We prove that the GSA mechanism can alleviate both the overfitting and the over-smoothing issues based on some recent technical developments. Experiments on multiple benchmark datasets illustrate both superior expressive power and less significant overfitting and over-smoothing problems for the GSA-augmented GCNs, which corroborate the intuitions and the theoretical results.

Towards Efficient 3D Point Cloud Scene Completion Via Novel Depth View Synthesis

Haiyan Wang, Liang Yang, Xuejian Rong, Ying-Li Tian

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Auto-TLDR; 3D Point Cloud Completion with Depth View Synthesis and Depth View synthesis

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3D point cloud completion has been a long-standing challenge at scale, and corresponding per-point supervised training strategies suffered from the cumbersome annotations. 2D supervision has recently emerged as a promising alternative for 3D tasks, but specific approaches for 3D point cloud completion still remain to be explored. To overcome these limitations, we propose an end-to-end method that directly lifts a single depth map to a completed point cloud. With one depth map as input, a multi-way novel depth view synthesis network (NDVNet) is designed to infer coarsely completed depth maps under various viewpoints. Meanwhile, a geometric depth perspective rendering module is introduced to utilize the raw input depth map to generate a re-projected depth map for each view. Therefore, the two parallelly generated depth maps for each view are further concatenated and refined by a depth completion network (DCNet). The final completed point cloud is fused from all refined depth views. Experimental results demonstrate the effectiveness of our proposed approach composed of aforementioned components, to produce high-quality state-of-the-art results on the popular SUNCG benchmark.

SECI-GAN: Semantic and Edge Completion for Dynamic Objects Removal

Francesco Pinto, Andrea Romanoni, Matteo Matteucci, Phil Torr

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Auto-TLDR; SECI-GAN: Semantic and Edge Conditioned Inpainting Generative Adversarial Network

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Image inpainting aims at synthesizing the missing content of damaged and corrupted images to produce visually realistic restorations; typical applications are in image restoration, automatic scene editing, super-resolution, and dynamic object removal. In this paper, we propose Semantic and Edge Conditioned Inpainting Generative Adversarial Network (SECI-GAN), an architecture that jointly exploits the high-level cues extracted by semantic segmentation and the fine-grained details captured by edge extraction to condition the image inpainting process. SECI-GAN is designed with a particular focus on recovering big regions belonging to the same object (e.g. cars or pedestrians) in the context of dynamic object removal from complex street views. To demonstrate the effectiveness of SECI-GAN, we evaluate our results on the Cityscapes dataset, showing that SECI-GAN is better than competing state-of-the-art models at recovering the structure and the content of the missing parts while producing consistent predictions.

Walk the Lines: Object Contour Tracing CNN for Contour Completion of Ships

André Peter Kelm, Udo Zölzer

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Auto-TLDR; Walk the Lines: A Convolutional Neural Network trained to follow object contours

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We develop a new contour tracing algorithm to enhance the results of the latest object contour detectors. The goal is to achieve a perfectly closed, single-pixel wide and detailed object contour, since this type of contour could be analyzed using methods such as Fourier descriptors. Convolutional Neural Networks (CNNs) are rarely used for contour tracing, and we see great potential in using their capabilities for this task. Therefore we present the Walk the Lines (WtL) algorithm: A standard regression CNN trained to follow object contours. As initial step, we train the CNN only on ship contours, but the principle is applicable to other objects. Input data are the image and the associated object contour prediction of the recently published RefineContourNet (RCN). The WtL gets the center pixel coordinates, which defines an input section, plus an angle for rotating this section. Ideally, the center pixel moves on the contour, while the angle describes upcoming directional contour changes. The WtL predicts its steps pixelwise in a selfrouting way. To obtain a complete object contour the WtL runs in parallel at different image locations and the traces of its individual paths are summed. In contrast to the comparable Non-Maximum Suppression (NMS) method, our approach produces connected contours with finer details. Finally, the object contour is binarized under the condition of being closed. In case all procedures work as desired, excellent ship segmentations with high IoUs are produced, showing details such as antennas and ship superstructures that are easily omitted by other segmentation methods.

Fast Implementation of 4-Bit Convolutional Neural Networks for Mobile Devices

Anton Trusov, Elena Limonova, Dmitry Slugin, Dmitry Nikolaev, Vladimir V. Arlazarov

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Auto-TLDR; Efficient Quantized Low-Precision Neural Networks for Mobile Devices

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Quantized low-precision neural networks are very popular because they require less computational resources for inference and can provide high performance, which is vital for real-time and embedded recognition systems. However, their advantages are apparent for FPGA and ASIC devices, while general-purpose processor architectures are not always able to perform low-bit integer computations efficiently. The most frequently used low-precision neural network model for mobile central processors is an 8-bit quantized network. However, in a number of cases, it is possible to use fewer bits for weights and activations, and the only problem is the difficulty of efficient implementation. We introduce an efficient implementation of 4-bit matrix multiplication for quantized neural networks and perform time measurements on a mobile ARM processor. It shows 2.9 times speedup compared to standard floating-point multiplication and is 1.5 times faster than 8-bit quantized one. We also demonstrate a 4-bit quantized neural network for OCR recognition on the MIDV-500 dataset. 4-bit quantization gives 95.0% accuracy and 48% overall inference speedup, while an 8-bit quantized network gives 95.4% accuracy and 39% speedup. The results show that 4-bit quantization perfectly suits mobile devices, yielding good enough accuracy and low inference time.

Quaternion Capsule Networks

Barış Özcan, Furkan Kınlı, Mustafa Furkan Kirac

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Auto-TLDR; Quaternion Capsule Networks for Object Recognition

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Capsules are grouping of neurons that allow to represent sophisticated information of a visual entity such as pose and features. In the view of this property, Capsule Networks outperform CNNs in challenging tasks like object recognition in unseen viewpoints, and this is achieved by learning the transformations between the object and its parts with the help of high dimensional representation of pose information. In this paper, we present Quaternion Capsules (QCN) where pose information of capsules and their transformations are represented by quaternions. Quaternions are immune to the gimbal lock, have straightforward regularization of the rotation representation for capsules, and require less number of parameters than matrices. The experimental results show that QCNs generalize better to novel viewpoints with fewer parameters, and also achieve on-par or better performances with the state-of-the-art Capsule architectures on well-known benchmarking datasets.

Edge-Aware Graph Attention Network for Ratio of Edge-User Estimation in Mobile Networks

Jiehui Deng, Sheng Wan, Xiang Wang, Enmei Tu, Xiaolin Huang, Jie Yang, Chen Gong

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Auto-TLDR; EAGAT: Edge-Aware Graph Attention Network for Automatic REU Estimation in Mobile Networks

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Estimating the Ratio of Edge-Users (REU) is an important issue in mobile networks, as it helps the subsequent adjustment of loads in different cells. However, existing approaches usually determine the REU manually, which are experience-dependent and labor-intensive, and thus the estimated REU might be imprecise. Considering the inherited graph structure of mobile networks, in this paper, we utilize a graph-based deep learning method for automatic REU estimation, where the practical cells are deemed as nodes and the load switchings among them constitute edges. Concretely, Graph Attention Network (GAT) is employed as the backbone of our method due to its impressive generalizability in dealing with networked data. Nevertheless, conventional GAT cannot make full use of the information in mobile networks, since it only incorporates node features to infer the pairwise importance and conduct graph convolutions, while the edge features that are actually critical in our problem are disregarded. To accommodate this issue, we propose an Edge-Aware Graph Attention Network (EAGAT), which is able to fuse the node features and edge features for REU estimation. Extensive experimental results on two real-world mobile network datasets demonstrate the superiority of our EAGAT approach to several state-of-the-art methods.