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Class subset selection for partial domain adaptation 32

Class subset selection for partial domain adaptation

引用

32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Zohrizadeh, Fariba Kheirandishfard, Mohsen Kamangar, Farhad Department of Computer Science and Engineering University of Texas Arlington United States

ISBN: (纸本)9781728125060

Domain adaptation is the task of transferring knowledge from a labeled source dataset to an unlabeled target dataset. Partial domain adaptation (PDA) investigates the scenarios in which the target label space is a subset of the source label space. The main purpose of the PDA is to identify the shared classes between the domains and promote learning transferable knowledge from these classes. Inspired by the idea of subset selection, we propose an adversarial PDA approach which aims to not only automatically select the most relevant subset of source domain classes but also ignore the samples that are less transferable across the domains. In the absence of target labels, the proposed approach is able to effectively learn domain-invariant feature representations, which in turn can facilitate and enhance the classification performance in the target domain. Empirical results on Office-31 and Office-Home datasets demonstrate the high potential of the proposed approach in addressing different partial domain adaptation tasks. © 2019 ieee computer Society. All rights reserved.

关键词： Set theory

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An Accurate Segmentation-Based Scene Text Detector with Context Attention and Repulsive Text Border

An Accurate Segmentation-Based Scene Text Detector with Cont...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Xi Liu Gaojing Zhou Rui Zhang Xiaolin Wei Meituan-Dianping Group Beijing China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Scene text detection is one of the most challenging problems in computer vision and has attracted great interest. In general, scene text detection methods are divided into two categories: detection-based and segmentation-based methods. Recently, the segmentation-based methods are more and more popular due to their superior performances and the advantages of detecting arbitrary-shape texts. However, there still exist the following problems: (a) the misclassfication of the unexpected texts, (b) the split of long text lines, (c) the failure of separating very close text instances. In this paper, we propose an accurate segmentation-based detector, which is equipped with context attention and repulsive text border. The context attention incorporates global channel attention, non-local self-attention and spatial attention to better exploit the global and local context, which can greatly increase the discriminative ability for pixels. Due to the enhancement of pixel-level features, false positives and the misdetections of long texts are reduced. Besides, for the purpose of solving very close text instance, a repulsive pixel link, which focuses on the relationships between pixels at the border, is proposed. Experiments on several standard benchmarks, including MSRA-TD500, ICDAR2015, ICDAR2017-MLT and CTW1500, validate the superiority of the proposed method.

关键词： Convolution Decoding Feature extraction Detectors computer vision Benchmark testing Object detection

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Using Mixture of Expert Models to Gain Insights into Semantic Segmentation

Using Mixture of Expert Models to Gain Insights into Semanti...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Svetlana Pavlitskaya Christian Hubschneider Michael Weber Ruby Moritz Fabian Hüger Peter Schlicht J. Marius Zöllner FZI Research Center for Information Technology Volkswagen Group Automation

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Not only correct scene understanding, but also ability to understand the decision making process of neural networks is essential for safe autonomous driving. Current work mainly focuses on uncertainty measures, often based on Monte Carlo dropout, to gain at least some insight into a models confidence. We investigate a mixture of experts architecture to achieve additional interpretability while retaining comparable result quality. By being able to use both the overall model output as well as retaining the possibility to take into account individual expert outputs, the agreement or disagreement between those individual outputs can be used to gain insights into the decision process. Expert networks are trained by splitting the input data into semantic subsets, e.g. corresponding to different driving scenarios, to become experts in those domains. An additional gating network that is also trained on the same input data is consequently used to weight the output of individual experts. We evaluate this mixture of expert setup on the A2D2 dataset and achieve similar results to a baseline FRRN network trained on all available data, while getting additional information.

关键词： Logic gates computer architecture Feature extraction Uncertainty Semantics Neural networks Task analysis

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Multi-Granularity Tracking with Modularlized Components for Unsupervised Vehicles Anomaly Detection

Multi-Granularity Tracking with Modularlized Components for ...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yingying Li Jie Wu Xue Bai Xipeng Yang Xiao Tan Guanbin Li Shilei Wen Hongwu Zhang Errui Ding Department of Computer Vision Technology (VIS) Baidu Inc Sun Yat-sen University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Anomaly detection on road traffic is a fundamental computer vision task and plays a critical role in video structure analysis and urban traffic analysis. Although it has attracted intense attention in recent years, it remains a very challenging problem due to the complexity of the traffic scene, the dense chaos of traffic flow and the lack of fine-grained abnormal labeled data. In this paper, we propose a multi-granularity tracking approach with modularized components to analyze traffic anomaly detection. The modularized framework consists of a detection module, a background modeling module, a mask extraction module, and a multi-granularity tracking algorithm. Concretely, a box-level tracking branch and a pixel-level tracking branch is employed respectively to make abnormal predictions. Each tracking branch helps to capture abnormal abstractions at different granularity levels and provide rich and complementary information for the concept learning of abnormal behaviors. Finally, a novel fusion and backtracking optimization is further performed to refine the abnormal predictions. The experimental results reveal that our framework is superior in the Track4 test set of the NVIDIA AI CITY 2020 CHALLENGE, which ranked first in this competition, with a 98.5% F1-score and 4.8737 root mean square error.

关键词： Anomaly detection Roads Task analysis Tracking Training Artificial intelligence Urban areas

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Multi-Stream CNN for Spatial Resource Allocation: a Crop Management Application

Multi-Stream CNN for Spatial Resource Allocation: a Crop Man...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Alexandre Barbosa Thiago Marinho Nicolas Martin Naira Hovakimyan University of Illinois at Urbana-Champaign Urbana IL USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Modeling the spatial structure of crop inputs is of great importance for accurate yield prediction. It is a fundamental step towards optimizing the spatial allocation of resources such as seed and fertilizer. We propose two distinct architectures of Multi-Stream Convolutional Neural Network (MSCNN) - Late Fusion (LF) and Early Fusion (EF) - to model yield response to seed and nutrient management. A study presents a comparison between proposed models with conventional 2D and 3D CNN architectures, and existing agronomy methods. The dataset used to train and test the models is constructed using on-farm experiment data from nine cornfields across the US together with multispectral satellite images. Results show that the MSCNN-LF achieved a 20% reduction of the prediction's mean squared error value when compared to a 3D CNN, and a 26% reduction when compared to a 2D CNN. An optimization algorithm uses the MSCNN-LF model's gradient to change the manageable inputs variables in a way the expected profit is maximized subject to resource constraints. It is shown that an increase of up to 5.2% on expected crop yield return is obtained when compared to usual management practices.

关键词： Agriculture Nitrogen computer architecture Data models Three-dimensional displays Soil Analytical models

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Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways

Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Weikai Tan Nannan Qin Lingfei Ma Ying Li Jing Du Guorong Cai Ke Yang Jonathan Li Department of Geography and Environmental Management University of Waterloo Waterloo ON Canada College of Computer Engineering Jimei University Xiamen China Department of Systems Design Engineering University of Waterloo Waterloo ON Canada

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Semantic segmentation of large-scale outdoor point clouds is essential for urban scene understanding in various applications, especially autonomous driving and urban high-definition (HD) mapping. With rapid developments of mobile laser scanning (MLS) systems, massive point clouds are available for scene understanding, but publicly accessible large-scale labeled datasets, which are essential for developing learning-based methods, are still limited. This paper introduces Toronto-3D, a large-scale urban outdoor point cloud dataset acquired by a MLS system in Toronto, Canada for semantic segmentation. This dataset covers approximately 1 km of point clouds and consists of about 78.3 million points with 8 labeled object classes. Baseline experiments for semantic segmentation were conducted and the results confirmed the capability of this dataset to train deep learning models effectively. Toronto-3D is released 1 to encourage new research, and the labels will be improved and updated with feedback from the research community.

关键词： Three-dimensional displays Semantics Roads Laser radar Sensors Machine learning Automobiles

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Explaining the pointnet: What has been learned inside the pointnet? 32

Explaining the pointnet: What has been learned inside the po...

引用

32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Zhang, Binbin Huang, Shikun Shen, Wen Wei, Zhihua Department of Computer Science and Technology Tongji University Shanghai China

ISBN: (纸本)9781728125060

In this work, we focus on explaining the PointNet [4], the first deep learning framework to directly handle 3D point clouds. We raise two issues based on the nature of PointNet and give solutions. First, we visualize the activation of point functions to examine the issue how global features represent different classes? Then, we propose a derivative of PointNet, named C-PointNet, to generate the class-attentive responce maps to explore that based on what information in the point cloud is the PointNet making a decision? The experiments on ModelNet40 demonstrate the efficacy of our work for getting better understanding of PointNet. © 2019 ieee computer Society. All rights reserved.

关键词： C (programming language)

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Fast Human Head and Shoulder Detection Using Convolutional Networks and RGBD Data

Fast Human Head and Shoulder Detection Using Convolutional N...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Wassim A. El Ahmar Farzan Erlik Nowruzi Robert Laganiere Department of Electrical Engineering and Computer Science University of Ottawa Ottawa Ontario Canada

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

We introduce a new real-time approach for human head and shoulder detection from RGB-D data based on a combination of image processing and deep learning approaches. Candidate head-top locations (CHL) are generated from a fast and accurate image processing algorithm that operates on depth data. We propose enhancements to the CHL algorithm making it three times faster. Various deep learning models are then evaluated for the tasks of classification and detection on the candidate head-top locations to regress the head bounding boxes and detect shoulder keypoints. We propose three different models based on convolutional neural networks for this problem. Experimental results for different architectures of our model are discussed. We also compare the performance of our models to other state of the art methods in terms of accuracy of detections and computational cost and show that our proposed models are on par with the state of the art in terms of precision-recall of head detection and precision of shoulders detection, with the biggest advantage of our models being in terms of computation time. We also analyze the effect of adding the depth channel on the performance of the network.

关键词： Head Proposals Magnetic heads Machine learning Three-dimensional displays Training Task analysis

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Eff-UNet: A Novel Architecture for Semantic Segmentation in Unstructured Environment

Eff-UNet: A Novel Architecture for Semantic Segmentation in ...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Bhakti Baheti Shubham Innani Suhas Gajre Sanjay Talbar SGGSIET Center of Excellence in Signal and Image Processing Nanded India

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Since the last few decades, the number of road causalities has seen continuous growth across the globe. Nowa-days intelligent transportation systems are being developed to enable safe and relaxed driving and scene understanding of the surrounding environment is an integral part of it. While several approaches are being developed for semantic scene segmentation based on deep learning and Convolutional Neural Network (CNN), these approaches assume well structured road infrastructure and driving environment. We focus our work on recent India Driving Lite Dataset (IDD), which contains data from unstructured driving environment and was hosted as an online challenge in NCVPRIPG 2019. We propose a novel architecture named as Eff-UNet which combines the effectiveness of compound scaled EfficientNet as the encoder for feature extraction with UNet decoder for reconstructing the fine-grained segmentation map. High level feature information as well as low level spatial information useful for precise segmentation are combined. The proposed architecture achieved 0.7376 and 0.6276 mean Intersection over Union (mIoU) on validation and test dataset respectively and won first prize in IDD lite segmentation challenge outperforming other approaches in the literature.

关键词： Image segmentation Semantics Roads Feature extraction Decoding Image resolution computer architecture

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Deep Lighting Environment Map Estimation from Spherical Panoramas

Deep Lighting Environment Map Estimation from Spherical Pano...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Vasileios Gkitsas Nikolaos Zioulis Federico Alvarez Dimitrios Zarpalas Petros Daras Centre for Research and Technology Hellas (CERTH) Information Technologies Institute (ITI) Signals Systems and Radiocommunications Department (SSRD) Universidad Politecnica de Madrid (UPM)

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Estimating a scene's lighting is a very important task when compositing synthetic content within real environments, with applications in mixed reality and postproduction. In this work we present a data-driven model that estimates an HDR lighting environment map from a single LDR monocular spherical panorama. In addition to being a challenging and ill-posed problem, the lighting estimation task also suffers from a lack of facile illumination ground truth data, a fact that hinders the applicability of data-driven methods. We approach this problem differently, exploiting the availability of surface geometry to employ image-based relighting as a data generator and supervision mechanism. This relies on a global Lambertian assumption that helps us overcome issues related to pre-baked lighting. We relight our training data and complement the model's supervision with a photometric loss, enabled by a differentiable image-based relighting technique. Finally, since we predict spherical spectral coefficients, we show that by imposing a distribution prior on the predicted coefficients, we can greatly boost performance. Code and models available at ***/DeepPanoramaLighting.

关键词： Lighting Estimation Task analysis Geometry Data models Image color analysis Rendering (computer graphics)

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