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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

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Widerperson: A diverse dataset for dense pedestrian detection in the wild

arXiv

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arXiv 2019年

作者： Zhang, Shifeng Xie, Yiliang Wan, Jun Xia, Hansheng Li, Stan Z. Guo, Guodong Beijing China Macau University of Science and Technology China United States Nanjing China Institute of Deep Learning Baidu Research and National Engineering Laboratory for Deep Learning Technology and Application

Pedestrian detection has achieved significant progress with the availability of existing benchmark datasets. However, there is a gap in the diversity and density between real world requirements and current pedestrian detection benchmarks: 1) most of existing datasets are taken from a vehicle driving through the regular traffic scenario, usually leading to insufficient diversity;2) crowd scenarios with highly occluded pedestrians are still under represented, resulting in low density. To narrow this gap and facilitate future pedestrian detection research, we introduce a large and diverse dataset named WiderPerson for dense pedestrian detection in the wild. This dataset involves five types of annotations in a wide range of scenarios, no longer limited to the traffic scenario. There are a total of 13, 382 images with 399, 786 annotations, i.e., 29.87 annotations per image, which means this dataset contains dense pedestrians with various kinds of occlusions. Hence, pedestrians in the proposed dataset are extremely challenging due to large variations in the scenario and occlusion, which is suitable to evaluate pedestrian detectors in the wild. We introduce an improved Faster R-CNN and the vanilla RetinaNet to serve as baselines for the new pedestrian detection benchmark. Several experiments are conducted on previous datasets including Caltech-USA and CityPersons to analyze the generalization capabilities of the proposed dataset and we achieve state-of-the-art performances on these previous datasets without bells and whistles. Finally, we analyze common failure cases and find the classification ability of pedestrian detector needs to be improved to reduce false alarm and miss detection rates. The proposed dataset is available at http://***/users/sfzhang/WiderPerson. Copyright © 2019, The Authors. All rights reserved.

关键词： Large dataset

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GINet: Graph interaction network for scene parsing

arXiv

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arXiv 2020年

作者： Wu, Tianyi Lu, Yu Zhu, Yu Zhang, Chuang Wu, Ming Ma, Zhanyu Guo, Guodong Institute of Deep Learning Baidu Research Beijing China National Engineering Laboratory for Deep Learning Technology and Application Beijing China Beijing University of Posts and Telecommunications Beijing China

Recently, context reasoning using image regions beyond local convolution has shown great potential for scene parsing. In this work, we explore how to incorperate the linguistic knowledge to promote context reasoning over image regions by proposing a Graph Interaction unit (GI unit) and a Semantic Context Loss (SC-loss). The GI unit is capable of enhancing feature representations of convolution networks over high-level semantics and learning the semantic coherency adaptively to each sample. Specifically, the dataset-based linguistic knowledge is first incorporated in the GI unit to promote context reasoning over the visual graph, then the evolved representations of the visual graph are mapped to each local representation to enhance the discriminated capability for scene parsing. GI unit is further improved by the SC-loss to enhance the semantic representations over the exemplar-based semantic graph. We perform full ablation studies to demonstrate the effectiveness of each component in our approach. Particularly, the proposed GINet outperforms the state-of-the-art approaches on the popular benchmarks, including Pascal-Context and COCO Stuff. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

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Dynamic Group Transformer: A General Vision Transformer Backbone with Dynamic Group Attention

arXiv

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arXiv 2022年

作者： Liu, Kai Wu, Tianyi Liu, Cong Guo, Guodong Sun Yat-Sen University Guangzhou China Institute of Deep Learning Baidu Research Beijing China National Engineering Laboratory for Deep Learning Technology and Application Beijing China

Recently, Transformers have shown promising performance in various vision tasks. To reduce the quadratic computation complexity caused by each query attending to all keys/values, various methods have constrained the range of attention within local regions, where each query only attends to keys/values within a hand-crafted window. However, these hand-crafted window partition mechanisms are data-agnostic and ignore their input content, so it is likely that one query maybe attends to irrelevant keys/values. To address this issue, we propose a Dynamic Group Attention (DG-Attention), which dynamically divides all queries into multiple groups and selects the most relevant keys/values for each group. Our DG-Attention can flexibly model more relevant dependencies without any spatial constraint that is used in hand-crafted window based attention. Built on the DG-Attention, we develop a general vision transformer backbone named Dynamic Group Transformer (DGT). Extensive experiments show that our models can outperform the state-of-the-art methods on multiple common vision tasks, including image classification, semantic segmentation, object detection, and instance segmentation. Copyright © 2022, The Authors. All rights reserved.

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RotPredictor: Unsupervised Canonical Viewpoint learning for Point Cloud Classification

RotPredictor: Unsupervised Canonical Viewpoint Learning for ...

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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

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Part-level Car Parsing and Reconstruction from a Single Street View

arXiv

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arXiv 2018年

作者： Geng, Qichuan Zhang, Hong Huang, Xinyu Wang, Sen Lu, Feixiang Cheng, Xinjing Zhou, Zhong Yang, Ruigang Beihang University Beijing China Baidu Research Beijing China National Engineering Laboratory of Deep Learning Technology and Application China

Part information has been shown to be resistant to occlusions and viewpoint changes, which is beneficial for various vision-related tasks. However, we found very limited work in car pose estimation and reconstruction from street views leveraging the part information. There are two major contributions in this paper. Firstly, we make the first attempt to build a framework to simultaneously estimate shape, translation, orientation, and semantic parts of cars in 3D space from a single street view. As it is labor-intensive to annotate semantic parts on real street views, we propose a specific approach to implicitly transfer part features from synthesized images to real street views. For pose and shape estimation, we propose a novel network structure that utilizes both part features and 3D losses. Secondly, we are the first to construct a high-quality dataset that contains 348 different car models with physical dimensions and part-level annotations based on global and local deformations. Given these models, we further generate 60K synthesized images with randomization of orientation, illumination, occlusion, and texture. Our results demonstrate that our part segmentation performance is significantly improved after applying our implicit transfer approach. Our network for pose and shape estimation achieves the state-of-the-art performance on the ApolloCar3D dataset and outperforms 3D-RCNN and deepMANTA by 12.57 and 8.91 percentage points in terms of mean A3DP-Abs [17, 3, 30]. Copyright © 2018, The Authors. All rights reserved.

关键词： Model automobiles

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

arXiv

引用

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)

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LAE : Long-Tailed Age Estimation 19th

LAE : Long-Tailed Age Estimation

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19th International Conference on Computer Analysis of Images and Patterns, CAIP 2021

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 Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science and Innovation 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

ISBN: (纸本)9783030891305

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. © 2021, Springer Nature Switzerland AG.

关键词： deep learning

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Detection of manganese nodule ore based on underwater hyperspectral imaging technology 6

Detection of manganese nodule ore based on underwater hypers...

引用

6th Conference on Frontiers in Optical Imaging and technology: applications of Imaging Technologies

作者： Shen, Mengling Men, Shaojie Liu, Bohan Li, Dewei Liu, Zhaojun School of Information Science and Engineering Shandong University China Key Laboratory of Laser and Infrared System Integration Technology Ministry of Education China Shandong Provincial Key Laboratory of Laser Technology and Application China National Deep Sea Center China

ISBN: (数字)9781510679757

ISBN: (纸本)9781510679740

Manganese nodules, widely distributed across the deep-sea floor, are emerging as a significant potential mineral resource. Addressing the need for improved exploration and classification methods, this study explores the application of underwater hyperspectral imaging technology for the detection and classification of manganese nodule ore and other rock types. The system, operating within a spectral range of 400 - 1000 nm and achieving a spectral resolution of less than 5 nm, captures the spectral characteristics and spatial information of manganese nodule ores. Different classifiers, including Spectral Angle Mapper (SAM), Support Vector Machine (SVM), and Convolutional Neural Networks (2D-CNN and 3D-CNN) to analyze the spectral data. Our results indicate that the four classifiers can effectively achieve ore classification, and CNN-based classifiers significantly outperform traditional SAM and SVM methods. The 2D-CNN model achieved the highest OA at 92.27%, closely followed by the 3D-CNN model at 91.77%. Our findings demonstrate the potential of underwater hyperspectral imaging combined with advanced machine learning techniques in marine mineral detection and environmental monitoring. © 2024 SPIE.

关键词： Support vector machines

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Knowledge Abstraction Matching for Medical Question Answering

Knowledge Abstraction Matching for Medical Question Answerin...

引用

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.

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