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AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection

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AutoShape: Real-Time Shape-Aware Monocular 3D Object Detecti...

International Conference on Computer Vision (ICCV)

作者： Zongdai Liu Dingfu Zhou Feixiang Lu Jin Fang Liangjun Zhang Robotics and Autonomous Driving Laboratory Baidu Research National Engineering Laboratory of Deep Learning Technology and Application China

ISBN: (纸本)9781665428132

Existing deep learning-based approaches for monocular 3D object detection in autonomous driving often model the object as a rotated 3D cuboid while the object’s geometric shape has been ignored. In this work, we propose an approach for incorporating the shape-aware 2D/3D constraints into the 3D detection framework. Specifically, we employ the deep neural network to learn distinguished 2D keypoints in the 2D image domain and regress their corresponding 3D coordinates in the local 3D object coordinate first. Then the 2D/3D geometric constraints are built by these correspondences for each object to boost the detection performance. For generating the ground truth of 2D/3D keypoints, an automatic model-fitting approach has been proposed by fitting the deformed 3D object model and the object mask in the 2D image. The proposed framework has been verified on the public KITTI dataset and the experimental results demonstrate that by using additional geometrical constraints the detection performance has been significantly improved as compared to the baseline method. More importantly, the proposed framework achieves state-of-the-art performance with real time. Data and code will be available at https://***/zongdai/AutoShape

关键词： Deformable models deep learning Solid modeling Three-dimensional displays Shape Neural networks Pipelines

CASIA-SURF: A Large-Scale Multi-Modal Benchmark for Face Anti-Spoofing

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IEEE Transactions on Biometrics, Behavior, and Identity Scie...

IEEE Transactions on Biometrics, Behavior, and Identity Science 2020年第2期2卷 182-193页

作者： Zhang, Shifeng Liu, Ajian Wan, Jun Liang, Yanyan Guo, Guodong Escalera, Sergio Escalante, Hugo Jair Li, Stan Z. National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China Faculty of Information Technology Macau University of Science and Technology 999078 China Baidu Research and National Engineering Laboratory for Deep Learning Technology and Application Institute of Deep Learning Beijing100085 China Óptica y Electrónica Instituto Nacional de Astrofísica Puebla08007 Mexico Computer Science Department CINVESTAV-Zacatenco Mexico City07360 Mexico University of Chinese Academy of Sciences Beijing100049 China Westlake University Hangzhou310024 China

Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face anti-spoofing benchmarks have limited number of subjects (≤170) and modalities (≤2), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and modalities. Specifically, it consists of 1,000 subjects with 21,000 videos and each sample has 3 modalities (i.e., RGB, Depth and IR). We also provide comprehensive evaluation metrics, diverse evaluation protocols, training/validation/testing subsets and a measurement tool, developing a new benchmark for face anti-spoofing. Moreover, we present a novel multi-modal multi-scale fusion method as a strong baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modality across different scales. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability. The dataset is available at https://***/***/face-anti-spoofing/welcome/challengecvpr2019?authuser=0. © 2019 IEEE.

关键词： Face recognition

DeLS-3D: deep localization and segmentation with a 3D semantic map

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arXiv 2018年

作者： Wang, Peng Yang, Ruigang Cao, Binbin Xu, Wei Lin, Yuanqing Baidu Research National Engineering Laboratory for Deep Learning Technology and Applications

For applications such as augmented reality, autonomous driving, self-localization/camera pose estimation and scene parsing are crucial technologies. In this paper, we propose a unified framework to tackle these two problems simultaneously. The uniqueness of our design is a sensor fusion scheme which integrates camera videos, motion sensors (GPS/IMU), and a 3D semantic map in order to achieve robustness and efficiency of the system. Specifically, we first have an initial coarse camera pose obtained from consumer-grade GPS/IMU, based on which a label map can be rendered from the 3D semantic map. Then, the rendered label map and the RGB image are jointly fed into a pose CNN, yielding a corrected camera pose. In addition, to incorporate temporal information, a multi-layer recurrent neural network (RNN) is further deployed improve the pose accuracy. Finally, based on the pose from RNN, we render a new label map, which is fed together with the RGB image into a segment CNN which produces per-pixel semantic label. In order to validate our approach, we build a dataset with registered 3D point clouds and video camera images. Both the point clouds and the images are semantically-labeled. Each video frame has ground truth pose from highly accurate motion sensors. We show that practically, pose estimation solely relying on images like PoseNet [25] may fail due to street view confusion, and it is important to fuse multiple sensors. Finally, various ablation studies are performed, which demonstrate the effectiveness of the proposed system. In particular, we show that scene parsing and pose estimation are mutually beneficial to achieve a more robust and accurate system. Copyright © 2018, The Authors. All rights reserved.

关键词： Rendering (computer graphics)

POEM: 1-bit point-wise operations based on expectation-maximization for efficient point cloud processing

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arXiv 2021年

作者： Xu, Sheng Li, Yanjing Zhao, Junhe Zhang, Baochang Guo, Guodong Beihang University Beijing China National Engineering Laboratory for Deep Learning Technology and Application Institute of Deep Learning Baidu Research Beijing China

Real-time point cloud processing is fundamental for lots of computer vision tasks, while still challenged by the computational problem on resource-limited edge devices. To address this issue, we implement XNOR-Net-based binary neural networks (BNNs) for an efficient point cloud processing, but its performance is severely suffered due to two main drawbacks, Gaussian-distributed weights and non-learnable scale factor. In this paper, we introduce point-wise operations based on Expectation-Maximization (POEM) into BNNs for efficient point cloud processing. The EM algorithm can efficiently constrain weights for a robust bi-modal distribution. We lead a well-designed reconstruction loss to calculate learnable scale factors to enhance the representation capacity of 1-bit fully-connected (Bi-FC) layers. Extensive experiments demonstrate that our POEM surpasses existing the state-of-the-art binary point cloud networks by a significant margin, up to 6.7%. © 2021, CC BY.

关键词： Maximum principle

Mapfusion: A general framework for 3D object detection with HDMaps

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arXiv 2021年

作者： Fang, Jin Zhou, Dingfu Song, Xibin Zhang, Liangjun Robotics and Autonomous Driving Laboratory Baidu Research China National Engineering Laboratory of Deep Learning Technology and Application China

3D object detection is a key perception component in autonomous driving. Most recent approaches are based on Lidar sensors only or fused with cameras. Maps (e.g., High Definition Maps), a basic infrastructure for intelligent vehicles, however, have not been well exploited for boosting object detection tasks. In this paper, we propose a simple but effective framework - MapFusion to integrate the map information into modern 3D object detector pipelines. In particular, we design a FeatureAgg module for HD Map feature extraction and fusion, and a MapSeg module as an auxiliary segmentation head for the detection backbone. Our proposed MapFusion is detector independent and can be easily integrated into different detectors. The experimental results of three different baselines on large public autonomous driving dataset demonstrate the superiority of the proposed framework. By fusing the map information, we can achieve 1.27 to 2.79 points improvements for mean Average Precision (mAP) on three strong 3d object detection baselines. © 2021, CC BY-NC-ND.

关键词： Object recognition

Knowledge Abstraction Matching for Medical Question Answering

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Knowledge Abstraction Matching for Medical Question Answerin...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jun Chen Jingbo Zhou Zhenhui Shi Bin Fan Chengliang Luo Baidu Inc Beijing China Business Intelligence Lab Baidu Research National Engineering Laboratory of Deep Learning Technology and Application China

Medical Question Answering (medical QA), which studies the problem of automatically answering patients' medical questions online, is one of the major applications of bioinformatics. Though many efforts have been made before, the medical QA system still deserves delicate algorithm optimization due to the serious application scenario and strict requirement for the answer quality. In this paper, we introduce a novel Knowledge Abstraction Matching (KAM) method for the medical QA problem. The intuition of KAM is that there are many frequent repeat text segments appearing in the answers across different questions. From this view, we propose a new method that consists of frequent segment N-gram mining, medical knowledge abstraction, medical segment matching and answer re-retrieval. KAM has been incorporated into baidu's enterprise medical QA system MelodyQA deployed on the backend of Muzhi Doctor. The evaluation shows that the proposed method can generate more quality answers for MelodyQA with a significant improvement of question coverage under acceptable accuracy.

关键词：

CATrans: Context and Affinity Transformer for Few-Shot Segmentation

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arXiv 2022年

作者： Zhang, Shan Wu, Tianyi Wu, Sitong Guo, Guodong Australian National University Canberra Australia Institute of Deep Learning Baidu Research Beijing China National Engineering Laboratory for Deep Learning Technology and Application Beijing China

Few-shot segmentation (FSS) aims to segment novel categories given scarce annotated support images. The crux of FSS is how to aggregate dense correlations between support and query images for query segmentation while being robust to the large variations in appearance and context. To this end, previous Transformer-based methods explore global consensus either on context similarity or affinity map between support-query pairs. In this work, we effectively integrate the context and affinity information via the proposed novel Context and Affinity Transformer (CATrans) in a hierarchical architecture. Specifically, the Relation-guided Context Transformer (RCT) propagates context information from support to query images conditioned on more informative support features. Based on the observation that a huge feature distinction between support and query pairs brings barriers for context knowledge transfer, the Relation-guided Affinity Transformer (RAT) measures attention-aware affinity as auxiliary information for FSS, in which the self-affinity is responsible for more reliable cross-affinity. We conduct experiments to demonstrate the effectiveness of the proposed model, outperforming the state-of-the-art methods. © 2022, CC BY.

关键词： Image segmentation

RotPredictor: Unsupervised Canonical Viewpoint learning for Point Cloud Classification

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RotPredictor: Unsupervised Canonical Viewpoint Learning for ...

International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT)

作者： Jin Fang Dingfu Zhou Xibin Song Shengze Jin Ruigang Yang Liangjun Zhang Baidu Research National Engineering Laboratory of Deep Learning Technology and Application China ETH Zürich Switzerland University of Kentucky

ISBN: (数字)9781728181288

ISBN: (纸本)9781728181295

Recently, significant progress has been achieved in analyzing the 3D point cloud with deep learning techniques. However, existing networks suffer from poor generalization and robustness to arbitrary rotations applied to the input point cloud. Different from traditional strategies that improve the rotation robustness with data augmentation or specifically designed spherical representation or harmonics-based kernels, we propose to rotate the point cloud into a canonical viewpoint for boosting the following downstream target task, e.g., object classification and part segmentation. Specifically, the canonical viewpoint is predicted by the network RotPredictor in an unsupervised way and the loss function is only built on the target task. Our RotPredictor satisfies the rotation equivariance property in (3) approximately and the predication output has the linear relationship with the applied rotation transformation. In addition, the RotPredictor is an independent plug and play module, which can be employed by any point-based deep learning framework without extra burden. Experimental results on the public model classification dataset ModelNet40 show the performance for all baselines can be boosted by integrating the proposed module. In addition, by adding our proposed module, we can achieve the state-of-the-art classification accuracy with 90.2% on the rotation-augmented ModelNet40 benchmark.

关键词： Three-dimensional displays Task analysis Robustness Convolution Two dimensional displays deep learning Solid modeling

Hierarchical Pyramid Diverse Attention Networks for Face Recognition

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Hierarchical Pyramid Diverse Attention Networks for Face Rec...

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Qiangchang Wang Tianyi Wu He Zheng Guodong Guo West Virginia University Morgantown USA Institute of Deep Learning Baidu Research Beijing China National Engineering Laboratory for Deep Learning Technology and Application Beijing China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

deep learning has achieved a great success in face recognition (FR), however, few existing models take hierarchical multi-scale local features into consideration. In this work, we propose a hierarchical pyramid diverse attention (HPDA) network. First, it is observed that local patches would play important roles in FR when the global face appearance changes dramatically. Some recent works apply attention modules to locate local patches automatically without relying on face landmarks. Unfortunately, without considering diversity, some learned attentions tend to have redundant responses around some similar local patches, while neglecting other potential discriminative facial parts. Meanwhile, local patches may appear at different scales due to pose variations or large expression changes. To alleviate these challenges, we propose a pyramid diverse attention (PDA) to learn multi-scale diverse local representations automatically and adaptively. More specifically, a pyramid attention is developed to capture multi-scale features. Meanwhile, a diverse learning is developed to encourage models to focus on different local patches and generate diverse local features. Second, almost all existing models focus on extracting features from the last convolutional layer, lacking of local details or small-scale face parts in lower layers. Instead of simple concatenation or addition, we propose to use a hierarchical bilinear pooling (HBP) to fuse information from multiple layers effectively. Thus, the HPDA is developed by integrating the PDA into the HBP. Experimental results on several datasets show the effectiveness of the HPDA, compared to the state-of-the-art methods.

关键词： Face Feature extraction Face recognition Handheld computers Machine learning Fuses Computational modeling