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Channel attention based iterative residual learning for depth map super-resolution

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arXiv 2020年

作者： Song, Xibin Dai, Yuchao Zhou, Dingfu Liu, Liu Li, Wei Li, Hongdong Yang, Ruigang Baidu Research National Engineering Laboratory of Deep Learning Technology and Application China Northwestern Polytechnical University China Shandong University China Australian National University Australia Australian Centre for Robotic Vision Australia University of Kentucky Kentucky United States

Despite the remarkable progresses made in deep-learning based depth map super-resolution (DSR), how to tackle real-world degradation in low-resolution (LR) depth maps remains a major challenge. Existing DSR model is generally trained and tested on synthetic dataset, which is very different from what would get from a real depth sensor. In this paper, we argue that DSR models trained under this setting are restrictive and not effective in dealing with real-world DSR tasks. We make two contributions in tackling real-world degradation of different depth sensors. First, we propose to classify the generation of LR depth maps into two types: non-linear downsampling with noise and interval downsampling, for which DSR models are learned correspondingly. Second, we propose a new framework for real-world DSR, which consists of four modules: 1) An iterative residual learning module with deep supervision to learn effective high-frequency components of depth maps in a coarse-to-fine manner;2) A channel attention strategy to enhance channels with abundant high-frequency components;3) A multi-stage fusion module to effectively re-exploit the results in the coarse-to-fine process;and 4) A depth refinement module to improve the depth map by TGV regularization and input loss. Extensive experiments on benchmarking datasets demonstrate the superiority of our method over current state-of-the-art DSR methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical resolving power

Kham dialect speech synthesis based on deep learning

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Kham dialect speech synthesis based on deep learning

2019 International Joint Conference on Information, Media, and engineering, IJCIME 2019

作者： Zhang, Weizhao Yang, Hongwu Bu, Xiaolong College of Physics and Electronic Engineering Engineering Research Center of Gansu Province for Intelligent Information Technology and Application Northwest Normal University Lanzhou China School of Educational Technology National and Provincial Joint Engineering Laboratory of Learning Analysis Technology in Online Education College of Physics and Electronic Engineering Northwest Normal University Lanzhou China

ISBN: (纸本)9781728155869

In this paper, we constructed speech synthesis corpus of Kham dialect. At the same time, we designed SAMP-Kham machine-readable phonetic label of Kham dialect, and proposed a framework of Kham dialect speech synthesis based on deep learning. The deep learning architecture includes deep neural network (DNN), hybrid long short-term memory (LSTM) and hybrid Bidirectional long short-term memory (BLSTM). The objective and subjective evaluations test showed that quality of synthesized speech of Kham dialect was satisfied. © 2019 IEEE.

关键词： deep neural networks

LAE : Long-tailed Age Estimation

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arXiv 2021年

作者： Bao, Zenghao Tan, Zichang Zhu, Yu Wan, Jun Ma, Xibo Lei, Zhen Guo, Guodong CBSR NLPR Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Institute of Deep Learning Baidu Research National Engineering Laboratory for Deep Learning Technology and Application

Facial age estimation is an important yet very challenging problem in computer vision. To improve the performance of facial age estimation, we first formulate a simple standard baseline and build a much strong one by collecting the tricks in pre-training, data augmentation, model architecture, and so on. Compared with the standard baseline, the proposed one significantly decreases the estimation errors. Moreover, long-tailed recognition has been an important topic in facial age datasets, where the samples often lack on the elderly and children. To train a balanced age estimator, we propose a two-stage training method named Long-tailed Age Estimation (LAE), which decouples the learning procedure into representation learning and classification. The effectiveness of our approach has been demonstrated on the dataset provided by organizers of Guess The Age Contest 2021. Copyright © 2021, The Authors. All rights reserved.

关键词： deep learning

Quasi-potential as an implicit regularizer for the loss function in the stochastic gradient descent.

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arXiv 2019年

作者： Hu, Wenqing Zhu, Zhanxing Xiong, Haoyi Huan, Jun Department of Mathematics and Statistics Missouri University of Science and Technology University of Missouri Rolla Peking University Beijing Institute of Big Data Research Beijing China Big Data Lab Baidu Inc. National Engineering Laboratory of Deep Learning Application and Technology

We interpret the variational inference of the Stochastic Gradient Descent (SGD) as minimizing a new potential function named the quasi-potential. We analytically construct the quasi-potential function in the case when the loss function is convex and admits only one global minimum point. We show in this case that the quasi-potential function is related to the noise covariance structure of SGD via a partial differential equation of Hamilton-Jacobi type. This relation helps us to show that anisotropic noise leads to faster escape than isotropic noise. We then consider the dynamics of SGD in the case when the loss function is non-convex and admits several different local minima. In this case, we demonstrate an example that shows how the noise covariance structure plays a role in "implicit regularization", a phenomenon in which SGD favors some particular local minimum points. This is done through the relation between the noise covariance structure and the quasi-potential function. Our analysis is based on Large Deviations Theory (LDT), and they are validated by numerical experiments. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

The speech synthesis of yi language based on DNN

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The speech synthesis of yi language based on DNN

2019 International Joint Conference on Information, Media, and engineering, IJCIME 2019

作者： Bu, Xiaolong Yang, Hongwu Zhang, Weizhao College of Physics and Electronic Engineering Eng. Research Center of Gansu Province for in Telligent Information Technology and Application Northwest Normal University Lanzhou China School of Educational Technology National and Provincial Joint Engineering Laboratory of Learning Analysis Technology in Online Education Northwest Normal University Lanzhou China

ISBN: (纸本)9781728155869

This paper is mainly about a speech synthesis system based on deep Neural Network (DNN) model of Yi languages, a kind of minority language in china. The system is composed of relatively complete text analysis of Yi, model training and speech synthesis module. Especially in front-end, the word segmentation, pause handling, word-to-phoneme conversion and label processing are used to analysis text of Yi language. We designed the question set for decision tree of DNN model training and used vocoder: WORLD for synthesis. The system achieves a relatively good Mean Opinion Score (MOS) of 3.93 by Yi undergraduates as evaluators compared with a MOS of 4.58 of original speech. To investigate the factors affecting the quality of synthesized Yi speech, this paper also objectively evaluates the performance of different training set and DNN model. The system successfully synthesized Yi speech for the first time and synthesized speech is relatively good as the result of an only complete minority language speech synthesis system. © 2019 IEEE.

关键词： deep neural networks

IDARTS: Interactive Differentiable Architecture Search

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IDARTS: Interactive Differentiable Architecture Search

International Conference on Computer Vision (ICCV)

作者： Song Xue Runqi Wang Baochang Zhang Tian Wang Guodong Guo David Doermann Beihang University Beijing China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing China Lobachevsky State University of Nizhni Novgorod Nizhni Novgorod Russian Federation National Engineering Laboratory for Deep Learning Technology and Application Institute of Deep Learning Baidu Research Beijing China University at Buffalo USA

ISBN: (纸本)9781665428132

Differentiable Architecture Search (DARTS) improves the efficiency of architecture search by learning the architecture and network parameters end-to-end. However, the intrinsic relationship between the architecture’s parameters is neglected, leading to a sub-optimal optimization process. The reason lies in the fact that the gradient descent method used in DARTS ignores the coupling relationship of the parameters and therefore degrades the optimization. In this paper, we address this issue by formulating DARTS as a bi-linear optimization problem and introducing an Interactive Differentiable Architecture Search (IDARTS). We first develop a backtracking backpropagation process, which can decouple the relationships of different kinds of parameters and train them in the same framework. The backtracking method coordinates the training of different parameters that fully explore their interaction and optimize training. We present experiments on the CIFAR10 and ImageNet datasets that demonstrate the efficacy of the IDARTS approach by achieving a top-1 accuracy of 76.52% on ImageNet without additional search cost vs. 75.8% with the state-of-the-art PC-DARTS.

关键词： Training Couplings Backpropagation Backtracking Computer vision Costs Computer architecture

A Unified Object Motion and Affinity Model for Online Multi-Object Tracking

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A Unified Object Motion and Affinity Model for Online Multi-...

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Junbo Yin Wenguan Wang Qinghao Meng Ruigang Yang Jianbing Shen Beijing Lab of Intelligent Information Technology School of Computer Science Beijing Institute of Technology China ETH Zurich Switzerland Baidu Research National Engineering Laboratory of Deep Learning Technology and Application China University of Kentucky Kentucky USA Inception Institute of Artificial Intelligence UAE

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Current popular online multi-object tracking (MOT) solutions apply single object trackers (SOTs) to capture object motions, while often requiring an extra affinity network to associate objects, especially for the occluded ones. This brings extra computational overhead due to repetitive feature extraction for SOT and affinity computation. Meanwhile, the model size of the sophisticated affinity network is usually non-trivial. In this paper, we propose a novel MOT framework that unifies object motion and affinity model into a single network, named UMA, in order to learn a compact feature that is discriminative for both object motion and affinity measure. In particular, UMA integrates single object tracking and metric learning into a unified triplet network by means of multi-task learning. Such design brings advantages of improved computation efficiency, low memory requirement and simplified training procedure. In addition, we equip our model with a task-specific attention module, which is used to boost task-aware feature learning. The proposed UMA can be easily trained end-to-end, and is elegant - requiring only one training stage. Experimental results show that it achieves promising performance on several MOT Challenge benchmarks.

关键词： Computational modeling Task analysis Target tracking Feature extraction Training

Joint 3D Instance Segmentation and Object Detection for Autonomous Driving

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Joint 3D Instance Segmentation and Object Detection for Auto...

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Dingfu Zhou Jin Fang Xibin Song Liu Liu Junbo Yin Yuchao Dai Hongdong Li Ruigang Yang Baidu Research National Engineering Laboratory of Deep Learning Technology and Application Beijing China Australian National University Canberra Australia Australian Centre for Robotic Vision Australia Beijing Institute of Technology Beijing China Northwestern Polytechnical University Xi'an China University of Kentucky Kentucky USA

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Currently, in Autonomous Driving (AD), most of the 3D object detection frameworks (either anchor- or anchor-free-based) consider the detection as a Bounding Box (BBox) regression problem. However, this compact representation is not sufficient to explore all the information of the objects. To tackle this problem, we propose a simple but practical detection framework to jointly predict the 3D BBox and instance segmentation. For instance segmentation, we propose a Spatial Embeddings (SEs) strategy to assemble all foreground points into their corresponding object centers. Base on the SE results, the object proposals can be generated based on a simple clustering strategy. For each cluster, only one proposal is generated. Therefore, the Non-Maximum Suppression (NMS) process is no longer needed here. Finally, with our proposed instance-aware ROI pooling, the BBox is refined by a second-stage network. Experimental results on the public KITTI dataset show that the proposed SEs can significantly improve the instance segmentation results compared with other feature embedding-based method. Meanwhile, it also outperforms most of the 3D object detectors on the KITTI testing benchmark.

关键词： Three-dimensional displays Object detection Two dimensional displays Proposals Feature extraction Shape Semantics

LiDAR-based online 3D video object detection with graph-based message passing and spatiotemporal transformer attention

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arXiv 2020年

作者： Yin, Junbo Shen, Jianbing Guan, Chenye Zhou, Dingfu Yang, Ruigang Beijing Lab of Intelligent Information Technology School of Computer Science Beijing Institute of Technology China Baidu Research National Engineering Laboratory of Deep Learning Technology and Application China Inception Institute of Artificial Intelligence United Arab Emirates University of Kentucky Kentucky United States

关键词： Object detection