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IAFA: Instance-aware feature aggregation for 3D object detection from a single image

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arXiv 2021年

作者： Zhou, Dingfu Song, Xibin Dai, Yuchao Yin, Junbo Lu, Feixiang Fang, Jin Liao, Miao Zhang, Liangjun Baidu Research China National Engineering Laboratory of Deep Learning Technology and Application Beijing China Northwestern Polytechnical University Xi’an China Beijing Institute of Technology Beijing China

3D object detection from a single image is an important task in Autonomous Driving (AD), where various approaches have been proposed. However, the task is intrinsically ambiguous and challenging as single image depth estimation is already an ill-posed problem. In this paper, we propose an instance-aware approach to aggregate useful information for improving the accuracy of 3D object detection with the following contributions. First, an instance-aware feature aggregation (IAFA) module is proposed to collect local and global features for 3D bounding boxes regression. Second, we empirically find that the spatial attention module can be well learned by taking coarse-level instance annotations as a supervision signal. The proposed module has significantly boosted the performance of the baseline method on both 3D detection and 2D bird-eye’s view of vehicle detection among all three categories. Third, our proposed method outperforms all single image-based approaches (even these methods trained with depth as auxiliary inputs) and achieves state-of-the-art 3D detection performance on the KITTI benchmark. © 2021, CC BY-NC-SA.

关键词： Object recognition

FusionPainting: Multimodal fusion with adaptive attention for 3D object detection

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arXiv 2021年

作者： Xu, Shaoqing Zhou, Dingfu Fang, Jin Yin, Junbo Bin, Zhou Zhang, Liangjun Beihang University Beijing100083 China Robotics and Autonomous Driving Laboratory Baidu Research National Engineering Laboratory of Deep Learning Technology and Application China Beijing Institute of Technology Beijing China

Accurate detection of obstacles in 3D is an essential task for autonomous driving and intelligent transportation. In this work, we propose a general multimodal fusion framework FusionPainting to fuse the 2D RGB image and 3D point clouds at a semantic level for boosting the 3D object detection task. Especially, the FusionPainting framework consists of three main modules: a multi-modal semantic segmentation module, an adaptive attention-based semantic fusion module, and a 3D object detector. First, semantic information is obtained for 2D image and 3D Lidar point clouds based on 2D and 3D segmentation approaches. Then the segmentation results from different sensors are adaptively fused based on the proposed attention-based semantic fusion module. Finally, the point clouds painted with the fused semantic label are sent to the 3D detector for obtaining the 3D objection results. The effectiveness of the proposed framework has been verified on the large-scale nuScenes detection benchmark by comparing with three different baselines. The experimental results show that the fusion strategy can significantly improve the detection performance compared to the methods using only point clouds, and the methods using point clouds only painted with 2D segmentation information. Furthermore, the proposed approach outperforms other state-of-the-art methods on the nuScenes testing benchmark. Code will be available at https://***/Shaoqing26/FusionPainting/. © 2021, CC BY-NC-ND.

关键词： Object recognition

Multi-Task Neural learning Architecture for End-to-End Identification of Helpful Reviews

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Multi-Task Neural Learning Architecture for End-to-End Ident...

International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Miao Fan Yue Feng Mingming Sun Ping Li Haifeng Wang Jianmin Wang National Engineering Laboratory of Deep Learning Technology and Application China School of Software Engineering Tsinghua University

ISBN: (纸本)9781538660522

Helpful reviews play a pivotal role in recommending desirable goods and accelerating purchase decisions of customers in e-commercial services. Given a large proportion of product reviews with unknown helpfulness/unhelpfulness, the research on automatic identification of helpful reviews has drawn much attention in recent years. However, state-of-the-art approaches still rely heavily on extracting heuristic text features from reviews with domain-specific knowledge. In this paper, we first introduce a multi-task neural learning (MTNL) architecture for identifying helpful reviews. The end-to-end neural architecture can learn to reconstruct effective features upon the raw input of words and even characters, and the multi-task learning paradigm helps to make more accurate predictions of helpful reviews based on a secondary task which fits the star ratings of reviews. We also build two datasets containing helpful/unhelpful reviews from different product categories in Amazon, and compare the performance of MTNL with several mainstream methods on both datasets. Experimental results confirm that MTNL outperforms the state-of-the-art approaches by a significant margin.

关键词： Task analysis Feature extraction Encoding Semantics Training Computer architecture

Semi-supervised hierarchical recurrent graph neural network for city-wide parking availability prediction

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arXiv 2019年

作者： Zhang, Weijia Liu, Hao Liu, Yanchi Zhou, Jingbo Xiong, Hui University of Science and Technology of China Hefei China Business Intelligence Lab Baidu Research National Engineering Laboratory of Deep Learning Technology and Application Beijing China Rutgers University United States

The ability to predict city-wide parking availability is crucial for the successful development of Parking Guidance and Information (PGI) systems. Indeed, the effective prediction of city-wide parking availability can improve parking efficiency, help urban planning, and ultimately alleviate city congestion. However, it is a non-trivial task for predicting citywide parking availability because of three major challenges: 1) the non-Euclidean spatial autocorrelation among parking lots, 2) the dynamic temporal autocorrelation inside of and between parking lots, and 3) the scarcity of information about real-time parking availability obtained from real-time sensors (e.g., camera, ultrasonic sensor, and GPS). To this end, we propose Semi-supervised Hierarchical Recurrent Graph Neural Network (SHARE) for predicting city-wide parking availability. Specifically, we first propose a hierarchical graph convolution structure to model non-Euclidean spatial autocorrelation among parking lots. Along this line, a contextual graph convolution block and a soft clustering graph convolution block are respectively proposed to capture local and global spatial dependencies between parking lots. Additionally, we adopt a recurrent neural network to incorporate dynamic temporal dependencies of parking lots. Moreover, we propose a parking availability approximation module to estimate missing real-time parking availabilities from both spatial and temporal domain. Finally, experiments on two real-world datasets demonstrate the prediction performance of SHARE outperforms seven state-of-the-art baselines. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

Generalizing from a few examples: A survey on few-shot learning

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arXiv 2019年

作者： WANG, YAQING YAO, QUANMING KWOK, JAMES T. NI, LIONEL M. Department of Computer Science and Engineering Hong Kong University of Science and Technology Business Intelligence Lab National Engineering Laboratory of Deep Learning Technology and Application Baidu Research 4Paradigm Inc.

Machine learning has been highly successful in data-intensive applications, but is often hampered when the data set is small. Recently, Few-Shot learning (FSL) is proposed to tackle this problem. Using prior knowledge, FSL can rapidly generalize to new tasks containing only a few samples with supervised information. In this paper, we conduct a thorough survey to fully understand FSL. Starting from a formal definition of FSL, we distinguish FSL from several relevant machine learning problems. We then point out that the core issue in FSL is that the empirical risk minimizer is unreliable. Based on how prior knowledge can be used to handle this core issue, we categorize FSL methods from three perspectives: (i) data, which uses prior knowledge to augment the supervised experience;(ii) model, which uses prior knowledge to reduce the size of the hypothesis space;and (iii) algorithm, which uses prior knowledge to alter the search for the best hypothesis in the given hypothesis space. With this taxonomy, we review and discuss the pros and cons of each category. Promising directions, in the aspects of the FSL problem setups, techniques, applications and theories, are also proposed to provide insights for future research. Copyright © 2019, The Authors. All rights reserved.

关键词： Surveys

Self-supervised monocular depth estimation for all day images using domain separation

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arXiv 2021年

作者： Liu, Lina Song, Xibin Wang, Mengmeng Liu, Yong Zhang, Liangjun Institute of Cyber-Systems and Control Zhejiang University China Baidu Research China Huzhou Institue of Zhejiang University China National Engineering Laboratory of Deep Learning Technology and Application China

Remarkable results have been achieved by DCNN based self-supervised depth estimation approaches. However, most of these approaches can only handle either day-time or night-time images, while their performance degrades for all-day images due to large domain shift and the variation of illumination between day and night images. To relieve these limitations, we propose a domain-separated network for self-supervised depth estimation of all-day images. Specifically, to relieve the negative influence of disturbing terms (illumination, etc.), we partition the information of day and night image pairs into two complementary sub-spaces: private and invariant domains, where the former contains the unique information (illumination, etc.) of day and night images and the latter contains essential shared information (texture, etc.). Meanwhile, to guarantee that the day and night images contain the same information, the domain-separated network takes the day-time images and corresponding night-time images (generated by GAN) as input, and the private and invariant feature extractors are learned by orthogonality and similarity loss, where the domain gap can be alleviated, thus better depth maps can be expected. Meanwhile, the reconstruction and photometric losses are utilized to estimate complementary information and depth maps effectively. Experimental results demonstrate that our approach achieves state-of-the-art depth estimation results for all-day images on the challenging Oxford RobotCar dataset, proving the superiority of our proposed approach. Code and data split are available at https://***/LINA-lln/ADDS-DepthNet. Copyright © 2021, The Authors. All rights reserved.

关键词： Textures

Coarse-to-Fine Cascaded Networks with Smooth Predicting for Video Facial Expression Recognition

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arXiv 2022年

作者： Xue, Fanglei Tan, Zichang Zhu, Yu Ma, Zhongsong Guo, Guodong University of Chinese Academy of Sciences Beijing China Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing China Institute of Deep Learning Baidu Research Beijing China National Engineering Laboratory for Deep Learning Technology and Application Beijing China

Facial expression recognition plays an important role in human-computer interaction. In this paper, we propose the Coarse-to-Fine Cascaded network with Smooth Predicting (CFC-SP) to improve the performance of facial expression recognition. CFC-SP contains two core components, namely Coarse-to-Fine Cascaded networks (CFC) and Smooth Predicting (SP). For CFC, it first groups several similar emotions to form a rough category, and then employs a network to conduct a coarse but accurate classification. Later, an additional network for these grouped emotions is further used to obtain fine-grained predictions. For SP, it improves the recognition capability of the model by capturing both universal and unique expression features. To be specific, the universal features denote the general characteristic of facial emotions within a period and the unique features denote the specific characteristic at this moment. Experiments on Aff-Wild2 show the effectiveness of the proposed CFSP. We achieved 3rd place in the Expression Classification Challenge of the 3rd Competition on Affective Behavior Analysis in-the-wild. The code will be released at https://***/BR-IDL/PaddleViT. © 2022, CC BY.

关键词： Forecasting

Self-supervised Monocular Depth Estimation for All Day Images using Domain Separation

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Self-supervised Monocular Depth Estimation for All Day Image...

International Conference on Computer Vision (ICCV)

作者： Lina Liu Xibin Song Mengmeng Wang Yong Liu Liangjun Zhang Institute of Cyber-Systems and Control Zhejiang University China Baidu Research China National Engineering Laboratory of Deep Learning Technology and Application China Huzhou Institue of Zhejiang University China

ISBN: (纸本)9781665428132

关键词： Computer vision Codes Estimation Lighting Generative adversarial networks Feature extraction Data mining

FaceScape: A Large-Scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction

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FaceScape: A Large-Scale High Quality 3D Face Dataset and De...

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Haotian Yang Hao Zhu Yanru Wang Mingkai Huang Qiu Shen Ruigang Yang Xun Cao Nanjing University Baidu Research National Engineering Laboratory for Deep Learning Technology and Applications China University of Kentucky Inceptio Inc.

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

In this paper, we present a large-scale detailed 3D face dataset, FaceScape, and propose a novel algorithm that is able to predict elaborate riggable 3D face models from a single image input. FaceScape dataset provides 18,760 textured 3D faces, captured from 938 subjects and each with 20 specific expressions. The 3D models contain the pore-level facial geometry that is also processed to be topologically uniformed. These fine 3D facial models can be represented as a 3D morphable model for rough shapes and displacement maps for detailed geometry. Taking advantage of the large-scale and high-accuracy dataset, a novel algorithm is further proposed to learn the expression-specific dynamic details using a deep neural network. The learned relationship serves as the foundation of our 3D face prediction system from a single image input. Different than the previous methods, our predicted 3D models are riggable with highly detailed geometry under different expressions. The unprecedented dataset and code will be released to public for research purpose.

关键词： Face Three-dimensional displays Solid modeling Shape Geometry Predictive models Cameras