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BP-Net: Deep Learning-Based Superpixel Segmentation for RGB-D Image

Bin Zhang, Xuejing Kang, Anlong Ming

Auto-TLDR; A Deep Learning-based Superpixel Segmentation Algorithm for RGB-D Image

Abstract Slides Poster

In this paper, we propose a deep learning-based superpixel segmentation algorithm for RGB-D image. The proposed deep neural network called BP-net is composed of boundary detection network (B-net) that exploits multiscale information from the depth image to extract the geometry edges of objects, and pixel labeling network (P-net) that extracts pixel features and generates superpixel. A boundary pass filter is proposed to combines the edge information and pixel features and ensures superpixel adheres better to geometry edge. To generate regular superpixel, we design a loss function which takes the shape regularity error and superpixel accuracy into account. In addition, for providing reasonable initial seeds, a new seeds initialization strategy is proposed, in which the density of seeds is investigated from a 2-manifolds space to reduce the number of superpixels that cover multiple objects in the region of richness texture. Experimental results demonstrate that our algorithm outperforms the existing state-of-the-art algorithms in terms of accuracy and shape regularity on the RGB-D dataset.

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Content-Sensitive Superpixels Based on Adaptive Regrowth

Xiaopeng Li, Junlin Xiong

Auto-TLDR; Adaptive Regrowth for Content-Sensitive Superpixels

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In this paper, we propose an efficient method to produce content-sensitive superpixels. Our method produces regular superpixels in relatively homogeneous regions and captures object boundaries in content-dense regions. Compared with the existing content-sensitive superpixel methods,a new adaptive regrowth strategy with an explicit boundary constraint is proposed.The boundary constraint limits the shapes and the sizes of superpixels to ensure semantic consistency. The adaptive regrowth strategy generates more superpixels to capture small objects in content-dense regions. Experiments on the BSDS500 benchmark show that our method outperforms the state-of-the-art superpixel methods in terms of content sensitivity and several standard evaluation metrics.

Generalized Shortest Path-Based Superpixels for Accurate Segmentation of Spherical Images

Rémi Giraud, Rodrigo Borba Pinheiro, Yannick Berthoumieu

Auto-TLDR; SPS: Spherical Shortest Path-based Superpixels

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Most of existing superpixel methods are designed to segment standard planar images as pre-processing for computer vision pipelines. Nevertheless, the increasing number of applications based on wide angle capture devices, mainly generating 360° spherical images, have enforced the need for dedicated superpixel approaches. In this paper, we introduce a new superpixel method for spherical images called SphSPS (for Spherical Shortest Path-based Superpixels). Our approach respects the spherical geometry and generalizes the notion of shortest path between a pixel and a superpixel center on the 3D spherical acquisition space. We show that the feature information on such path can be efficiently integrated into our clustering framework and jointly improves the respect of object contours and the shape regularity. To relevantly evaluate this last aspect in the spherical space, we also generalize a planar global regularity metric. Finally, the proposed SphSPS method obtains significantly better performances than both planar and spherical recent superpixel approaches on the reference 360 o spherical panorama segmentation dataset.

Deep Superpixel Cut for Unsupervised Image Segmentation

Qinghong Lin, Weichan Zhong

Auto-TLDR; Deep Superpixel Cut for Deep Unsupervised Image Segmentation

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Image segmentation, one of the most critical vision tasks, has been studied for many years. Most of the early algorithms are unsupervised methods, which use hand-crafted features to divide the image into many regions. Recently, owing to the great success of deep learning technology, CNNs based methods showing superior performance in image segmentation. However, these methods rely on a large number of human annotations, which are expensive to collect. In this paper, we propose a deep unsupervised method for image segmentation, which borrowed the ideas of classical graph partitioning. Our approach contains the following two stages. First, a Superpixel Guided Autoencoder (SGAE) is designed to learn the deep embedding and smooth the image simultaneously, then the smoothed image passed to generate superpixels. Second, based on the learned embedding, we propose a novel segmentation algorithm called Deep Superpixel Cut(DSC), which measures the deep similarity between superpixels and then adaptively partitions the superpixels into perceptual regions. Experimental results on the BSDS500 dataset demonstrate the effectiveness of the proposed method

Superpixel-Based Refinement for Object Proposal Generation

Christian Wilms, Simone Frintrop

Auto-TLDR; Superpixel-based Refinement of AttentionMask for Object Segmentation

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Precise segmentation of objects is an important problem in tasks like class-agnostic object proposal generation or instance segmentation. Deep learning-based systems usually generate segmentations of objects based on coarse feature maps, due to the inherent downsampling in CNNs. This leads to segmentation boundaries not adhering well to the object boundaries in the image. To tackle this problem, we introduce a new superpixel-based refinement approach on top of the state-of-the-art object proposal system AttentionMask. The refinement utilizes superpixel pooling for feature extraction and a novel superpixel classifier to determine if a high precision superpixel belongs to an object or not. Our experiments show an improvement of up to 26.0% in terms of average recall compared to original AttentionMask. Furthermore, qualitative and quantitative analyses of the segmentations reveal significant improvements in terms of boundary adherence for the proposed refinement compared to various deep learning-based state-of-the-art object proposal generation systems.

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

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

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.

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

Matteo Terreran, Elia Bonetto, Stefano Ghidoni

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

Abstract Slides Poster Similar

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.

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

Hangtao Feng, Lu Zhang, Xu Yang, Zhiyong Liu

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

Abstract Slides Poster Similar

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.

Fused 3-Stage Image Segmentation for Pleural Effusion Cell Clusters

Sike Ma, Meng Zhao, Hao Wang, Fan Shi, Xuguo Sun, Shengyong Chen, Hong-Ning Dai

Auto-TLDR; Coarse Segmentation of Stained and Stained Unstained Cell Clusters in pleural effusion using 3-stage segmentation method

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The appearance of tumor cell clusters in pleural effusion is usually a vital sign of cancer metastasis. Segmentation, as an indispensable basis, is of crucial importance for diagnosing, chemical treatment, and prognosis in patients. However, accurate segmentation of unstained cell clusters containing more detailed features than the fluorescent staining images remains to be a challenging problem due to the complex background and the unclear boundary. Therefore, in this paper, we propose a fused 3-stage image segmentation algorithm, namely Coarse segmentation-Mapping-Fine segmentation (CMF) to achieve unstained cell clusters from whole slide images. Firstly, we establish a tumor cell cluster dataset consisting of 107 sets of images, with each set containing one unstained image, one stained image, and one ground-truth image. Then, according to the features of the unstained and stained cell clusters, we propose a three-stage segmentation method: 1) Coarse segmentation on stained images to extract suspicious cell regions-Region of Interest (ROI); 2) Mapping this ROI to the corresponding unstained image to get the ROI of the unstained image (UI-ROI); 3) Fine Segmentation using improved automatic fuzzy clustering framework (AFCF) on the UI-ROI to get precise cell cluster boundaries. Experimental results on 107 sets of images demonstrate that the proposed algorithm can achieve better performance on unstained cell clusters with an F1 score of 90.40%.

Two-Stage Adaptive Object Scene Flow Using Hybrid CNN-CRF Model

Congcong Li, Haoyu Ma, Qingmin Liao

Auto-TLDR; Adaptive object scene flow estimation using a hybrid CNN-CRF model and adaptive iteration

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Scene flow estimation based on stereo sequences is a comprehensive task relevant to disparity and optical flow. Some existing methods are time-consuming and often fail in the presence of reflective surfaces. In this paper, we propose a two-stage adaptive object scene flow estimation method using a hybrid CNN-CRF model (ACOSF), which benefits from high-quality features and the structured modelling capability. Meanwhile, in order to balance the computational efficiency and accuracy, we employ adaptive iteration for energy function optimization, which is flexible and efficient for various scenes. Besides, we utilize high-quality pixel selection to reduce the computation time with only a slight decrease in accuracy. Our method achieves competitive results with the state-of-the-art, which ranks second on the challenging KITTI 2015 scene flow benchmark.

Multi-Scale Residual Pyramid Attention Network for Monocular Depth Estimation

Jing Liu, Xiaona Zhang, Zhaoxin Li, Tianlu Mao

Auto-TLDR; Multi-scale Residual Pyramid Attention Network for Monocular Depth Estimation

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Monocular depth estimation is a challenging problem in computer vision and is crucial for understanding 3D scene geometry. Recently, deep convolutional neural networks (DCNNs) based methods have improved the estimation accuracy significantly. However, existing methods fail to consider complex textures and geometries in scenes, thereby resulting in loss of local details, distorted object boundaries, and blurry reconstruction. In this paper, we proposed an end-to-end Multi-scale Residual Pyramid Attention Network (MRPAN) to mitigate these problems.First,we propose a Multi-scale Attention Context Aggregation (MACA) module, which consists of Spatial Attention Module (SAM) and Global Attention Module (GAM). By considering the position and scale correlation of pixels from spatial and global perspectives, the proposed module can adaptively learn the similarity between pixels so as to obtain more global context information of the image and recover the complex structure in the scene. Then we proposed an improved Residual Refinement Module (RRM) to further refine the scene structure, giving rise to deeper semantic information and retain more local details. Experimental results show that our method achieves more promisin performance in object boundaries and local details compared with other state-of-the-art methods.

Partially Supervised Multi-Task Network for Single-View Dietary Assessment

Ya Lu, Thomai Stathopoulou, Stavroula Mougiakakou

Auto-TLDR; Food Volume Estimation from a Single Food Image via Geometric Understanding and Semantic Prediction

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Food volume estimation is an essential step in the pipeline of dietary assessment and demands the precise depth estimation of the food surface and table plane. Existing methods based on computer vision require either multi-image input or additional depth maps, reducing convenience of implementation and practical significance. Despite the recent advances in unsupervised depth estimation from a single image, the achieved performance in the case of large texture-less areas needs to be improved. In this paper, we propose a network architecture that jointly performs geometric understanding (i.e., depth prediction and 3D plane estimation) and semantic prediction on a single food image, enabling a robust and accurate food volume estimation regardless of the texture characteristics of the target plane. For the training of the network, only monocular videos with semantic ground truth are required, while the depth map and 3D plane ground truth are no longer needed. Experimental results on two separate food image databases demonstrate that our method performs robustly on texture-less scenarios and is superior to unsupervised networks and structure from motion based approaches, while it achieves comparable performance to fully-supervised methods.

Incorporating Depth Information into Few-Shot Semantic Segmentation

Yifei Zhang, Desire Sidibe, Olivier Morel, Fabrice Meriaudeau

Auto-TLDR; RDNet: A Deep Neural Network for Few-shot Segmentation Using Depth Information

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Few-shot segmentation presents a significant challenge for semantic scene understanding under limited supervision. Namely, this task targets at generalizing the segmentation ability of the model to new categories given a few samples. In order to obtain complete scene information, we extend the RGB-centric methods to take advantage of complementary depth information. In this paper, we propose a two-stream deep neural network based on metric learning. Our method, known as RDNet, learns class-specific prototype representations within RGB and depth embedding spaces, respectively. The learned prototypes provide effective semantic guidance on the corresponding RGB and depth query image, leading to more accurate performance. Moreover, we build a novel outdoor scene dataset, known as Cityscapes-3i, using labeled RGB images and depth images from the Cityscapes dataset. We also perform ablation studies to explore the effective use of depth information in few-shot segmentation tasks. Experiments on Cityscapes-3i show that our method achieves promising results with visual and complementary geometric cues from only a few labeled examples.

Global-Local Attention Network for Semantic Segmentation in Aerial Images

Minglong Li, Lianlei Shan, Weiqiang Wang

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.

Siamese Graph Convolution Network for Face Sketch Recognition

Liang Fan, Xianfang Sun, Paul Rosin

Auto-TLDR; A novel Siamese graph convolution network for face sketch recognition

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In this paper, we present a novel Siamese graph convolution network (GCN) for face sketch recognition. To build a graph from an image, we utilize a deep learning method to detect the image edges, and then use a superpixel method to segment the edge image. Each segmented superpixel region is taken as a node, and each pair of adjacent regions forms an edge of the graph. Graphs from both a face sketch and a face photo are input into the Siamese GCN for recognition. A deep graph matching method is used to share messages between cross-modal graphs in this model. Experiments show that the GCN can obtain high performance on several face photo-sketch datasets, including seen and unseen face photo-sketch datasets. It is also shown that the model performance based on the graph structure representation of the data using the Siamese GCN is more stable than a Siamese CNN model.

Dynamic Guided Network for Monocular Depth Estimation

Xiaoxia Xing, Yinghao Cai, Yiping Yang, Dayong Wen

Auto-TLDR; DGNet: Dynamic Guidance Upsampling for Self-attention-Decoding for Monocular Depth Estimation

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Self-attention or encoder-decoder structure has been widely used in deep neural networks for monocular depth estimation tasks. The former mechanism are capable to capture long-range information by computing the representation of each position by a weighted sum of the features at all positions, while the latter networks can capture structural details information by gradually recovering the spatial information. In this work, we combine the advantages of both methods. Specifically, our proposed model, DGNet, extends EMANet Network by adding an effective decoder module to refine the depth results. In the decoder stage, we further design dynamic guidance upsampling which uses local neighboring information of low-level features guide coarser depth to upsample. In this way, dynamic guidance upsampling generates content-dependent and spatially-variant kernels for depth upsampling which makes full use of spatial details information from low-level features. Experimental results demonstrate that our method obtains higher accuracy and generates the desired depth map.

Machine-Learned Regularization and Polygonization of Building Segmentation Masks

Stefano Zorzi, Ksenia Bittner, Friedrich Fraundorfer

Auto-TLDR; Automatic Regularization and Polygonization of Building Segmentation masks using Generative Adversarial Network

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We propose a machine learning based approach for automatic regularization and polygonization of building segmentation masks. Taking an image as input, we first predict building segmentation maps exploiting generic fully convolutional network (FCN). A generative adversarial network (GAN) is then involved to perform a regularization of building boundaries to make them more realistic, i.e., having more rectilinear outlines which construct right angles if required. This is achieved through the interplay between the discriminator which gives a probability of input image being true and generator that learns from discriminator’s response to create more realistic images. Finally, we train the backbone convolutional neural network (CNN) which is adapted to predict sparse outcomes corresponding to building corners out of regularized building segmentation results. Experiments on three building segmentation datasets demonstrate that the proposed method is not only capable of obtaining accurate results, but also of producing visually pleasing building outlines parameterized as polygons.

Two-Level Attention-Based Fusion Learning for RGB-D Face Recognition

Hardik Uppal, Alireza Sepas-Moghaddam, Michael Greenspan, Ali Etemad

Auto-TLDR; Fused RGB-D Facial Recognition using Attention-Aware Feature Fusion

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With recent advances in RGB-D sensing technologies as well as improvements in machine learning and fusion techniques, RGB-D facial recognition has become an active area of research. A novel attention aware method is proposed to fuse two image modalities, RGB and depth, for enhanced RGB-D facial recognition. The proposed method first extracts features from both modalities using a convolutional feature extractor. These features are then fused using a two layer attention mechanism. The first layer focuses on the fused feature maps generated by the feature extractor, exploiting the relationship between feature maps using LSTM recurrent learning. The second layer focuses on the spatial features of those maps using convolution. The training database is preprocessed and augmented through a set of geometric transformations, and the learning process is further aided using transfer learning from a pure 2D RGB image training process. Comparative evaluations demonstrate that the proposed method outperforms other state-of-the-art approaches, including both traditional and deep neural network-based methods, on the challenging CurtinFaces and IIIT-D RGB-D benchmark databases, achieving classification accuracies over 98.2% and 99.3% respectively. The proposed attention mechanism is also compared with other attention mechanisms, demonstrating more accurate results.

Triplet-Path Dilated Network for Detection and Segmentation of General Pathological Images

Jiaqi Luo, Zhicheng Zhao, Fei Su, Limei Guo

Auto-TLDR; Triplet-path Network for One-Stage Object Detection and Segmentation in Pathological Images

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Deep learning has been widely applied in the field of medical image processing. However, compared with flourishing visual tasks in natural images, the progress achieved in pathological images is not remarkable, and detection and segmentation, which are among basic tasks of computer vision, are regarded as two independent tasks. In this paper, we make full use of existing datasets and construct a triplet-path network using dilated convolutions to cooperatively accomplish one-stage object detection and nuclei segmentation for general pathological images. First, in order to meet the requirement of detection and segmentation, a novel structure called triplet feature generation (TFG) is designed to extract high-resolution and multiscale features, where features from different layers can be properly integrated. Second, considering that pathological datasets are usually small, a location-aware and partially truncated loss function is proposed to improve the classification accuracy of datasets with few images and widely varying targets. We compare the performance of both object detection and instance segmentation with state-of-the-art methods. Experimental results demonstrate the effectiveness and efficiency of the proposed network on two datasets collected from multiple organs.

Coarse to Fine: Progressive and Multi-Task Learning for Salient Object Detection

Dong-Goo Kang, Sangwoo Park, Joonki Paik

Auto-TLDR; Progressive and mutl-task learning scheme for salient object detection

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Most deep learning-based salient object detection (SOD) methods tried to manipulate the convolution block to effectively capture the context of object. In this paper, we propose a novel method, called progressive and mutl-task learning scheme, to extract the context of object by only manipulating the learning scheme without changing the network architecture. The progressive learning scheme is a method to grow the decoder progressively in the train phase. In other words, starting from easier low-resolution layers, it gradually adds high-resolution layers. Although the progressive learning successfullyl captures the context of object, its output boundary tends to be rough. To solve this problem, we also propose a multi-task learning (MTL) scheme that processes the object saliency map and contour in a single network jointly. The proposed MTL scheme trains the network in an edge-preserved direction through an auxiliary branch that learns contours. The proposed a learning scheme can be combined with other convolution block manipulation methods. Extensive experiments on five datasets show that the proposed method performs best compared with state-of-the-art methods in most cases.

CT-UNet: An Improved Neural Network Based on U-Net for Building Segmentation in Remote Sensing Images

Huanran Ye, Sheng Liu, Kun Jin, Haohao Cheng

Auto-TLDR; Context-Transfer-UNet: A UNet-based Network for Building Segmentation in Remote Sensing Images

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With the proliferation of remote sensing images, how to segment buildings more accurately in remote sensing images is a critical challenge. First, the high resolution leads to blurred boundaries in the extracted building maps. Second, the similarity between buildings and background results in intra-class inconsistency. To address these two problems, we propose an UNet-based network named Context-Transfer-UNet (CT-UNet). Specifically, we design Dense Boundary Block (DBB). Dense Block utilizes reuse mechanism to refine features and increase recognition capabilities. Boundary Block introduces the low-level spatial information to solve the fuzzy boundary problem. Then, to handle intra-class inconsistency, we construct Spatial Channel Attention Block (SCAB). It combines context space information and selects more distinguishable features from space and channel. Finally, we propose a novel loss function to enhance the purpose of loss by adding evaluation indicator. Based on our proposed CT-UNet, we achieve 85.33% mean IoU on the Inria dataset and 91.00% mean IoU on the WHU dataset, which outperforms our baseline (U-Net ResNet-34) by 3.76% and Web-Net by 2.24%.

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

André Peter Kelm, Udo Zölzer

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.

3D Semantic Labeling of Photogrammetry Meshes Based on Active Learning

Mengqi Rong, Shuhan Shen, Zhanyi Hu

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.

Delivering Meaningful Representation for Monocular Depth Estimation

Doyeon Kim, Donggyu Joo, Junmo Kim

Auto-TLDR; Monocular Depth Estimation by Bridging the Context between Encoding and Decoding

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Monocular depth estimation plays a key role in 3D scene understanding, and a number of recent papers have achieved significant improvements using deep learning based algorithms. Most papers among them proposed methods that use a pre-trained network as a deep feature extractor and then decode the obtained features to create a depth map. In this study, we focus on how to use this encoder-decoder structure to deliver meaningful representation throughout the entire network. We propose a new network architecture with our suggested modules to create a more accurate depth map by bridging the context between the encoding and decoding phase. First, we place the pyramid block at the bottleneck of the network to enlarge the view and convey rich information about the global context to the decoder. Second, we suggest a skip connection with the fuse module to aggregate the encoder and decoder feature. Finally, we validate our approach on the NYU Depth V2 and KITTI datasets. The experimental results prove the efficacy of the suggested model and show performance gains over the state-of-the-art model.

DEN: Disentangling and Exchanging Network for Depth Completion

You-Feng Wu, Vu-Hoang Tran, Ting-Wei Chang, Wei-Chen Chiu, Ching-Chun Huang

Auto-TLDR; Disentangling and Exchanging Network for Depth Completion

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In this paper, we tackle the depth completion problem. Conventional depth sensors usually produce incomplete depth maps due to the property of surface reflection, especially for the window areas, metal surfaces, and object boundaries. However, we observe that the corresponding RGB images are still dense and preserve all of the useful structural information. This brings us to the question of whether we can borrow this structural information from RGB images to inpaint the corresponding incomplete depth maps. In this paper, we answer that question by proposing a Disentangling and Exchanging Network (DEN) for depth completion. The network is designed based on an assumption that after suitable feature disentanglement, RGB images and depth maps share a common domain for representing structural information. So we firstly disentangle both RGB and depth images into domain-invariant content parts, which contain structural information, and domain-specific style parts. Then, by exchanging the complete structural information extracted from RGB image with incomplete information extracted from depth map, we can generate the complete version of depth map. Furthermore, to address the mixed-depth problem, a newly proposed depth representation is applied. By modeling depth estimation as a classification problem coupled with coefficient estimation, blurry edges are enhanced in the depth map. At last, we have implemented ablation experiments to verify the effectiveness of our proposed DEN model. The results also demonstrate the superiority of DEN over some state-of-the-art approaches.

Edge-Aware Monocular Dense Depth Estimation with Morphology

Zhi Li, Xiaoyang Zhu, Haitao Yu, Qi Zhang, Yongshi Jiang

Auto-TLDR; Spatio-Temporally Smooth Dense Depth Maps Using Only a CPU

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Dense depth maps play an important role in Computer Vision and AR (Augmented Reality). For CV applications, a dense depth map is the cornerstone of 3D reconstruction allowing real objects to be precisely displayed in the computer. And Dense depth maps can handle correct occlusion relationships between virtual content and real objects for better user experience in AR. However, the complicated computation limits the development of computing dense depth maps. We present a novel algorithm that produces low latency, spatio-temporally smooth dense depth maps using only a CPU. The depth maps exhibit sharp discontinuities at depth edges in low computational complexity ways. Our algorithm obtains the sparse SLAM reconstruction first, then extracts coarse depth edges from a down-sampled RGB image by morphology operations. Next, we thin the depth edges and align them with image edges. Finally, a Warm-Start initialization scheme and an improved optimization solver are adopted to accelerate convergence. We evaluate our proposal quantitatively and the result shows improvements on the accuracy of depth map with respect to other state-of-the-art and baseline techniques.

Semi-Supervised Deep Learning Techniques for Spectrum Reconstruction

Adriano Simonetto, Vincent Parret, Alexander Gatto, Piergiorgio Sartor, Pietro Zanuttigh

Auto-TLDR; hyperspectral data estimation from RGB data using semi-supervised learning

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State-of-the-art approaches for the estimation of hyperspectral images (HSI) from RGB data are mostly based on deep learning techniques but due to the lack of training data their performances are limited to uncommon scenarios where a large hyperspectral database is available. In this work we present a family of novel deep learning schemes for hyperspectral data estimation able to work when the hyperspectral information at our disposal is limited. Firstly, we introduce a learning scheme exploiting a physical model based on the backward mapping to the RGB space and total variation regularization that can be trained with a limited amount of HSI images. Then, we propose a novel semi-supervised learning scheme able to work even with just a few pixels labeled with hyperspectral information. Finally, we show that the approach can be extended to a transfer learning scenario. The proposed techniques allow to reach impressive performances while requiring only some HSI images or just a few pixels for the training.

S-VoteNet: Deep Hough Voting with Spherical Proposal for 3D Object Detection

Yanxian Chen, Huimin Ma, Xi Li, Xiong Luo

Auto-TLDR; S-VoteNet: 3D Object Detection with Spherical Bounded Box Prediction

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Current 3D object detection methods adopt an analogous box prediction structure with the 2D methods, which predict center and size of the object simultaneously in a box regression procedure, leading to the poor performance of 3D detector to a great extent. In this work, we propose S-VoteNet, which converts the prediction of 3D bounding box into two parts: center prediction and size prediction. By introducing a novel spherical proposal, S-VoteNet uses vote groups to predict the center and radius of object rather than all parameters of 3D bounding box. The prediction of radius is used to constrain the object size, and the radius-based spherical center loss is applied to measure the geometric distance between the proposal and ground-truth. To make better use of the geometric information provided by point cloud, S-VoteNet gathers seed points whose corresponding votes are within the vote groups for seed group generation. Seed groups are then consumed for box size regression and orientation estimation. By decoupling the localization and size estimation, our method effectively reduces the regression pressure of the 3D detector. Experimental results on SUN RGB-D 3D detection benchmark demonstrate that our S-VoteNet achieves state-of-the-art performance by using only point cloud as input.

End-To-End Multi-Task Learning for Lung Nodule Segmentation and Diagnosis

Wei Chen, Qiuli Wang, Dan Yang, Xiaohong Zhang, Chen Liu, Yucong Li

Auto-TLDR; A novel multi-task framework for lung nodule diagnosis based on deep learning and medical features

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Computer-Aided Diagnosis (CAD) systems for lung nodule diagnosis based on deep learning have attracted much attention in recent years. However, most existing methods ignore the relationships between the segmentation and classification tasks, which leads to unstable performances. To address this problem, we propose a novel multi-task framework, which can provide lung nodule segmentation mask, malignancy prediction, and medical features for interpretable diagnosis at the same time. Our framework mainly contains two sub-network: (1) Multi-Channel Segmentation Sub-network (MSN) for lung nodule segmentation, and (2) Joint Classification Sub-network (JCN) for interpretable lung nodule diagnosis. In the proposed framework, we use U-Net down-sampling processes for extracting low-level deep learning features, which are shared by two sub-networks. The JCN forces the down-sampling processes to learn better lowlevel deep features, which lead to a better construct of segmentation masks. Meanwhile, two additional channels constructed by OTSU and super-pixel (SLIC) methods, are utilized as the guideline of the feature extraction. The proposed framework takes advantages of deep learning methods and classical methods, which can significantly improve the performances of all tasks. We evaluate the proposed framework on public dataset LIDCIDRI. Our framework achieves a promising Dice score of 86.43% in segmentation, 87.07% in malignancy level prediction, and convincing results in interpretable medical feature predictions.

Real-Time Monocular Depth Estimation with Extremely Light-Weight Neural Network

Mian Jhong Chiu, Wei-Chen Chiu, Hua-Tsung Chen, Jen-Hui Chuang

Auto-TLDR; Real-Time Light-Weight Depth Prediction for Obstacle Avoidance and Environment Sensing with Deep Learning-based CNN

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Obstacle avoidance and environment sensing are crucial applications in autonomous driving and robotics. Among all types of sensors, RGB camera is widely used in these applications as it can offer rich visual contents with relatively low-cost, and using a single image to perform depth estimation has become one of the main focuses in resent research works. However, prior works usually rely on highly complicated computation and power-consuming GPU to achieve such task; therefore, we focus on developing a real-time light-weight system for depth prediction in this paper. Based on the well-known encoder-decoder architecture, we propose a supervised learning-based CNN with detachable decoders that produce depth predictions with different scales. We also formulate a novel log-depth loss function that computes the difference of predicted depth map and ground truth depth map in log space, so as to increase the prediction accuracy for nearby locations. To train our model efficiently, we generate depth map and semantic segmentation with complex teacher models. Via a series of ablation studies and experiments, it is validated that our model can efficiently performs real-time depth prediction with only 0.32M parameters, with the best trained model outperforms previous works on KITTI dataset for various evaluation matrices.

6D Pose Estimation with Correlation Fusion

Yi Cheng, Hongyuan Zhu, Ying Sun, Cihan Acar, Wei Jing, Yan Wu, Liyuan Li, Cheston Tan, Joo-Hwee Lim

Auto-TLDR; Intra- and Inter-modality Fusion for 6D Object Pose Estimation with Attention Mechanism

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6D object pose estimation is widely applied in robotic tasks such as grasping and manipulation. Prior methods using RGB-only images are vulnerable to heavy occlusion and poor illumination, so it is important to complement them with depth information. However, existing methods using RGB-D data cannot adequately exploit consistent and complementary information between RGB and depth modalities. In this paper, we present a novel method to effectively consider the correlation within and across both modalities with attention mechanism to learn discriminative and compact multi-modal features. Then, effective fusion strategies for intra- and inter-correlation modules are explored to ensure efficient information flow between RGB and depth. To our best knowledge, this is the first work to explore effective intra- and inter-modality fusion in 6D pose estimation. The experimental results show that our method can achieve the state-of-the-art performance on LineMOD and YCBVideo dataset. We also demonstrate that the proposed method can benefit a real-world robot grasping task by providing accurate object pose estimation.

Enhanced Vote Network for 3D Object Detection in Point Clouds

Min Zhong, Gang Zeng

Auto-TLDR; A Vote Feature Enhancement Network for 3D Bounding Box Prediction

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In this work, we aim to estimate 3D bounding boxes by voting to object centers and then groups and aggregates the votes to generate 3D box proposals and semantic classes of objects. However, due to the sparse and unstructured nature of the point clouds, we face some challenges when directly predicting bounding box from the vote feature: the sparse vote feature may lack some necessary semantic and context information. To address the challenges, we propose a vote feature enhancement network that aims to encode semantic-aware information and aggravate global context for the vote feature. Specifically, we learn the point-wise semantic information and supplement it to the vote feature, and we also encode the pairwise relations to collect the global context. Experiments on two large datasets of real 3D scans, ScanNet and SUN RGB-D, demonstrate that our method can achieve excellent 3D detection results.

Quantization in Relative Gradient Angle Domain for Building Polygon Estimation

Yuhao Chen, Yifan Wu, Linlin Xu, Alexander Wong

Auto-TLDR; Relative Gradient Angle Transform for Building Footprint Extraction from Remote Sensing Data

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Building footprint extraction in remote sensing data benefits many important applications, such as urban planning and population estimation. Recently, rapid development of Convolutional Neural Networks (CNNs) and open-sourced high resolution satellite building image datasets have pushed the performance boundary further for automated building extractions. However, CNN approaches often generate imprecise building morphologies including noisy edges and round corners. In this paper, we leverage the performance of CNNs, and propose a module that uses prior knowledge of building corners to create angular and concise building polygons from CNN segmentation outputs. We describe a new transform, Relative Gradient Angle Transform (RGA Transform) that converts object contours from time vs. space to time vs. angle. We propose a new shape descriptor, Boundary Orientation Relation Set (BORS), to describe angle relationship between edges in RGA domain, such as orthogonality and parallelism. Finally, we develop an energy minimization framework that makes use of the angle relationship in BORS to straighten edges and reconstruct sharp corners, and the resulting corners create a polygon. Experimental results demonstrate that our method refines CNN output from a rounded approximation to a more clear-cut angular shape of the building footprint.

Learning to Segment Clustered Amoeboid Cells from Brightfield Microscopy Via Multi-Task Learning with Adaptive Weight Selection

Rituparna Sarkar, Suvadip Mukherjee, Elisabeth Labruyere, Jean-Christophe Olivo-Marin

Auto-TLDR; Supervised Cell Segmentation from Microscopy Images using Multi-task Learning in a Multi-Task Learning Paradigm

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Detecting and segmenting individual cells from microscopy images is critical to various life science applications. Traditional cell segmentation tools are often ill-suited for applications in brightfield microscopy due to poor contrast and intensity heterogeneity, and only a small subset are applicable to segment cells in a cluster. In this regard, we introduce a novel supervised technique for cell segmentation in a multi-task learning paradigm. A combination of a multi-task loss, based on the region and cell boundary detection, is employed for an improved prediction efficiency of the network. The learning problem is posed in a novel min-max framework which enables adaptive estimation of the hyper-parameters in an automatic fashion. The region and cell boundary predictions are combined via morphological operations and active contour model to segment individual cells. The proposed methodology is particularly suited to segment touching cells from brightfield microscopy images without manual interventions. Quantitatively, we observe an overall Dice score of 0.93 on the validation set, which is an improvement of over 15.9% on a recent unsupervised method, and outperforms the popular supervised U-net algorithm by at least 5.8% on average.

A Benchmark Dataset for Segmenting Liver, Vasculature and Lesions from Large-Scale Computed Tomography Data

Bo Wang, Zhengqing Xu, Wei Xu, Qingsen Yan, Liang Zhang, Zheng You

Auto-TLDR; The Biggest Treatment-Oriented Liver Cancer Dataset for Segmentation

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How to build a high-performance liver-related computer assisted diagnosis system is an open question of great interest. However, the performance of the state-of-art algorithm is always limited by the amount of data and quality of the label. To address this problem, we propose the biggest treatment-oriented liver cancer dataset for liver surgery and treatment planning. This dataset provides 216 cases (totally about 268K frames) scanned images in contrast-enhanced computed tomography (CT). We labeled all the CT images with the liver, liver vasculature and liver tumor segmentation ground truth for train and tune segmentation algorithms in advance. Based on that, we evaluate several recent and state-of-the-art segmentation algorithms, including 7 deep learning methods, on CT sequences. All results are compared to reference segmentations five error metrics that highlight different aspects of segmentation accuracy. In general, compared with previous datasets, our dataset is really a challenging dataset. To our knowledge, the proposed dataset and benchmark allow for the first time systematic exploration of such issues, and will be made available to allow for further research in this field.

One Step Clustering Based on A-Contrario Framework for Detection of Alterations in Historical Violins

Alireza Rezaei, Sylvie Le Hégarat-Mascle, Emanuel Aldea, Piercarlo Dondi, Marco Malagodi

Auto-TLDR; A-Contrario Clustering for the Detection of Altered Violins using UVIFL Images

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Preventive conservation is an important practice in Cultural Heritage. The constant monitoring of the state of conservation of an artwork helps us reduce the risk of damage and number of interventions necessary. In this work, we propose a probabilistic approach for the detection of alterations on the surface of historical violins based on an a-contrario framework. Our method is a one step NFA clustering solution which considers grey-level and spatial density information in one background model. The proposed method is robust to noise and avoids parameter tuning and any assumption about the quantity of the worn out areas. We have used as input UV induced fluorescence (UVIFL) images for considering details not perceivable with visible light. Tests were conducted on image sequences included in the ``Violins UVIFL imagery'' dataset. Results illustrate the ability of the algorithm to distinguish the worn area from the surrounding regions. Comparisons with the state of the art clustering methods shows improved overall precision and recall.

Boundary-Aware Graph Convolution for Semantic Segmentation

Hanzhe Hu, Jinshi Cui, Jinshi Hongbin Zha

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.

Weight Estimation from an RGB-D Camera in Top-View Configuration

Marco Mameli, Marina Paolanti, Nicola Conci, Filippo Tessaro, Emanuele Frontoni, Primo Zingaretti

Auto-TLDR; Top-View Weight Estimation using Deep Neural Networks

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The development of so-called soft-biometrics aims at providing information related to the physical and behavioural characteristics of a person. This paper focuses on bodyweight estimation based on the observation from a top-view RGB-D camera. In fact, the capability to estimate the weight of a person can be of help in many different applications, from health-related scenarios to business intelligence and retail analytics. To deal with this issue, a TVWE (Top-View Weight Estimation) framework is proposed with the aim of predicting the weight. The approach relies on the adoption of Deep Neural Networks (DNNs) that have been trained on depth data. Each network has also been modified in its top section to replace classification with prediction inference. The performance of five state-of-art DNNs has been compared, namely VGG16, ResNet, Inception, DenseNet and Efficient-Net. In addition, a convolutional auto-encoder has also been included for completeness. Considering the limited literature in this domain, the TVWE framework has been evaluated on a new publicly available dataset: “VRAI Weight estimation Dataset”, which also collects, for each subject, labels related to weight, gender, and height. The experimental results have demonstrated that the proposed methods are suitable for this task, bringing different and significant insights for the application of the solution in different domains.

FOANet: A Focus of Attention Network with Application to Myocardium Segmentation

Zhou Zhao, Elodie Puybareau, Nicolas Boutry, Thierry Geraud

Auto-TLDR; FOANet: A Hybrid Loss Function for Myocardium Segmentation of Cardiac Magnetic Resonance Images

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In myocardium segmentation of cardiac magnetic resonance images, ambiguities often appear near the boundaries of the target domains due to tissue similarities. To address this issue, we propose a new architecture, called FOANet, which can be decomposed in three main steps: a localization step, a Gaussian-based contrast enhancement step, and a segmentation step. This architecture is supplied with a hybrid loss function that guides the FOANet to study the transformation relationship between the input image and the corresponding label in a threelevel hierarchy (pixel-, patch- and map-level), which is helpful to improve segmentation and recovery of the boundaries. We demonstrate the efﬁciency of our approach on two public datasets in terms of regional and boundary segmentations.

A Multi-Task Contextual Atrous Residual Network for Brain Tumor Detection & Segmentation

Ngan Le, Kashu Yamazaki, Quach Kha Gia, Thanh-Dat Truong, Marios Savvides

Auto-TLDR; Contextual Brain Tumor Segmentation Using 3D atrous Residual Networks and Cascaded Structures

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In recent years, deep neural networks have achieved state-of-the-art performance in a variety of recognition and segmentation tasks in medical imaging including brain tumor segmentation. We investigate that segmenting brain tumor is facing to the imbalanced data problem where the number of pixels belonging to background class (non tumor pixel) is much larger than the number of pixels belonging to foreground class (tumor pixel). To address this problem, we propose a multi-task network which is formed as a cascaded structure and designed to share the feature maps. Our model consists of two targets, i.e., (i) effectively differentiating brain tumor regions and (ii) estimating brain tumor masks. The first task is performed by our proposed contextual brain tumor detection network, which plays the role of an attention gate and focuses on the region around brain tumor only while ignore the background (non tumor area). Instead of processing every pixel, our contextual brain tumor detection network only processes contextual regions around ground-truth instances and this strategy helps to produce meaningful regions proposals. The second task is built upon a 3D atrous residual network and under an encode-decode network in order to effectively segment both large and small objects (brain tumor). Our 3D atrous residual network is designed with a skip connection to enables the gradient from the deep layers to be directly propagated to shallow layers, thus, features of different depths are preserved and used for refining each other. In order to incorporate larger contextual information in volume MRI data, our network is designed by 3D atrous convolution with various kernel sizes, which enlarges the receptive field of filters. Our proposed network has been evaluated on various datasets including BRATS2015, BRATS2017 and BRATS2018 datasets with both validation set and testing set. Our performance has been benchmarked by both region-based metrics and surface-based metrics. We also have conducted comparisons against state-of-the-art approaches.

Semantic Segmentation Refinement Using Entropy and Boundary-guided Monte Carlo Sampling and Directed Regional Search

Zitang Sun, Sei-Ichiro Kamata, Ruojing Wang, Weili Chen

Auto-TLDR; Directed Region Search and Refinement for Semantic Segmentation

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Semantic segmentation requires both large receptive field and accurate spatial information. Despite existing methods based on fully convolutional network have greatly improved the accuracy, the prediction results still do not show satisfactory on small objects and boundary regions. We propose a refinement algorithm to improve the result generated by front network. Our method takes a modified U-shape network to generate both of segmentation mask and semantic boundary, which are used as inputs of refinement algorithm. We creatively introduce information entropy to represent the confidence of the neural network's prediction corresponding to each pixel. The information entropy combined with the semantic boundary can capture those unpredictable pixels with low-confidence through Monte Carlo sampling. Each selected pixel will be used as initial seeds for directed region search and refinement. Our purpose is to search the neighbor high-confidence regions according to the initial seeds. The re-labeling approach is based on high-confidence results. Particularly, different from general region growing methods, our method adopts a directed region search strategy based on gradient descent to find the high-confidence region effectively. Our method improves the performance both on Cityscapes and PASCAL VOC datasets. In the evaluation of segmentation accuracy of some small objects, our method surpasses most of state of the art methods.

Extending Single Beam Lidar to Full Resolution by Fusing with Single Image Depth Estimation

Yawen Lu, Yuxing Wang, Devarth Parikh, Guoyu Lu

Auto-TLDR; Self-supervised LIDAR for Low-Cost Depth Estimation

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Depth estimation is playing an important role in indoor and outdoor scene understanding, autonomous driving, augmented reality and many other tasks. Vehicles and robotics are able to use active illumination sensors such as LIDAR to receive high precision depth estimation. However, high-resolution Lidars are usually too expensive, which limits its massive production on various applications. Though single beam LIDAR enjoys the benefits of low cost, one beam depth sensing is not usually sufficient to perceive the surrounding environment in many scenarios. In this paper, we propose a learning-based framework to explore to replicate similar or even higher performance as costly LIDARs with our designed self-supervised network and a low-cost single-beam LIDAR. After the accurate calibration with a visible camera, the single beam LIDAR can adjust the scale uncertainty of the depth map estimated by the visible camera. The adjusted depth map enjoys the benefits of high resolution and sensing accuracy as high beam LIDAR and maintains low-cost as single beam LIDAR. Thus we can achieve similar sensing effect of high beam LIDAR with more than a 50-100 times cheaper price (e.g., \$80000 Velodyne HDL-64E LIDAR v.s. \$1000 SICK TIM-781 2D LIDAR and normal camera). The proposed approach is verified on our collected dataset and public dataset with superior depth-sensing performance.

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

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.

Coarse-To-Fine Foreground Segmentation Based on Co-Occurrence Pixel-Block and Spatio-Temporal Attention Model

Xinyu Liu, Dong Liang

Auto-TLDR; Foreground Segmentation from coarse to Fine Using Co-occurrence Pixel-Block Model for Dynamic Scene

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Foreground segmentation in dynamic scene is an important task in video surveillance. The unsupervised background subtraction method based on background statistics modeling has difficulties in updating. On the other hand, the supervised foreground segmentation method based on deep learning relies on the large-scale of accurately annotated training data, which limits its cross-scene performance. In this paper, we propose a foreground segmentation method from coarse to fine. First, a across-scenes trained Spatio-Temporal Attention Model (STAM) is used to achieve coarse segmentation, which does not require training on specific scene. Then the coarse segmentation is used as a reference to help Co-occurrence Pixel-Block Model (CPB) complete the fine segmentation, and at the same time help CPB to update its background model. This method is more flexible than those deep-learning-based methods which depends on the specific-scene training, and realizes the accurate online dynamic update of the background model. Experimental results on WallFlower and LIMU validate our method outperforms STAM, CPB and other methods of participating in comparison.

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

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

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.

Kernel-Based LIME with Feature Dependency Sampling

Sheng Shi, Yangzhou Du, Fan Wei

Auto-TLDR; Local Interpretable Model-agnostic Explanation with Feature Dependency Sampling

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While deep learning makes significant achievements in Artificial Intelligence (AI), the lack of transparency has limited its broad application in various vertical domains. Explainability is not only a gateway between AI and society, but also a powerful feature to detect flaw of the models and bias of the data. Local Interpretable Model-agnostic Explanation (LIME) is a widely-accepted technique that explains the predictions of any classifier faithfully by learning an interpretable model locally around the predicted instance. However, the sampling operation in the standard implementation of LIME is defective. Perturbed samples are generated from a uniform distribution, ignoring the complicated correlation between features. Moreover, as the local decision boundary is non-linear for most complex networks, linear approximation may produce serious errors. This paper proposes an high-interpretability and high-fidelity local explanation method, known as Kernel-based LIME with Feature Dependency Sampling (KLFDS). Given an instance being explained, KLFDS enhances interpretability by feature sampling with intrinsic dependency. Besides, KLFDS improves the local explanation fidelity by approximating nonlinear boundary of local decision. We evaluate our method with image classification tasks and results show that KLFDS's explanation of the back-box model achieves much better performance than original LIME in terms of interpretability and fidelity.

Adaptive Feature Fusion Network for Gaze Tracking in Mobile Tablets

Yiwei Bao, Yihua Cheng, Yunfei Liu, Feng Lu

Auto-TLDR; Adaptive Feature Fusion Network for Multi-stream Gaze Estimation in Mobile Tablets

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Recently, many multi-stream gaze estimation methods have been proposed. They estimate gaze from eye and face appearances and achieve reasonable accuracy. However, most of the methods simply concatenate the features extracted from eye and face appearance. The feature fusion process has been ignored. In this paper, we propose a novel Adaptive Feature Fusion Network (AFF-Net), which performs gaze tracking task in mobile tablets. We stack two-eye feature maps and utilize Squeeze-and-Excitation layers to adaptively fuse two-eye features based on different eye features. Meanwhile, we also propose Adaptive Group Normalization to recalibrate eye features with the guidance of face appearance characteristics. Extensive experiments on both GazeCapture and MPIIFaceGaze datasets demonstrate consistently superior performance of the proposed method.

Point In: Counting Trees with Weakly Supervised Segmentation Network

Pinmo Tong, Shuhui Bu, Pengcheng Han

Auto-TLDR; Weakly Tree counting using Deep Segmentation Network with Localization and Mask Prediction

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For tree counting tasks, since traditional image processing methods require expensive feature engineering and are not end-to-end frameworks, this will cause additional noise and cannot be optimized overall, so this method has not been widely used in recent trends of tree counting application. Recently, many deep learning based approaches are designed for this task because of the powerful feature extracting ability. The representative way is bounding box based supervised method, but time-consuming annotations are indispensable for them. Moreover, these methods are difficult to overcome the occlusion or overlap. To solve this problem, we propose a weakly tree counting network (WTCNet) based on deep segmentation network with only point supervision. It can simultaneously complete tree counting with localization and output mask of each tree at the same time. We first adopt a novel feature extractor network (FENet) to get features of input images, and then an effective strategy is introduced to deal with different mask predictions. In the end, we propose a basic localization guidance accompany with rectification guidance to train the network. We create two different datasets and select an existing challenging plant dataset to evaluate our method on three different tasks. Experimental results show the good performance improvement of our method compared with other existing methods. Further study shows that our method has great potential to reduce human labor and provide effective ground-truth masks and the results show the superiority of our method over the advanced methods.

MFPP: Morphological Fragmental Perturbation Pyramid for Black-Box Model Explanations

Qing Yang, Xia Zhu, Jong-Kae Fwu, Yun Ye, Ganmei You, Yuan Zhu

Auto-TLDR; Morphological Fragmental Perturbation Pyramid for Explainable Deep Neural Network

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Deep neural networks (DNNs) have recently been applied and used in many advanced and diverse tasks, such as medical diagnosis, automatic driving, etc. Due to the lack of transparency of the deep models, DNNs are often criticized for their prediction that cannot be explainable by human. In this paper, we propose a novel Morphological Fragmental Perturbation Pyramid (MFPP) method to solve the Explainable AI problem. In particular, we focus on the black-box scheme, which can identify the input area responsible for the output of the DNN without having to understand the internal architecture of the DNN. In the MFPP method, we divide the input image into multi-scale fragments and randomly mask out fragments as perturbation to generate a saliency map, which indicates the significance of each pixel for the prediction result of the black box model. Compared with the existing input sampling perturbation method, the pyramid structure fragment has proved to be more effective. It can better explore the morphological information of the input image to match its semantic information, and does not need any value inside the DNN. We qualitatively and quantitatively prove that MFPP meets and exceeds the performance of state-of-the-art (SOTA) black-box interpretation method on multiple DNN models and datasets.