检索结果-内蒙古大学图书馆

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

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.

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

来源：评论

学校读者我要写书评

暂无评论

IoU loss for 2D/3D object detection

arXiv

引用

arXiv 2019年

作者： Zhou, Dingfu Fang, Jin Song, Xibin Guan, Chenye Yin, Junbo Dai, Yuchao Yang, Ruigang Baidu Research National Engineering Laboratory of Deep Learning Technology and Application China Beijing Lab of Intelligent Information Technology School of Computer Science Beijing Institute of Technology China Northwestern Polytechnical University Xi'an China

In 2D/3D object detection task, Intersection-over-Union (IoU) has been widely employed as an evaluation metric to evaluate the performance of different detectors in the testing stage. However, during the training stage, the common distance loss (e.g., L1 or L2) is often adopted as the loss function to minimize the discrepency between the predicted and ground truth Bounding Box (Bbox). To eliminate the performance gap between training and testing, the IoU loss has been introduced for 2D object detection in [1] and [2]. Unfortunately, all these approaches only work for axis-aligned 2D Bboxes, which cannot be applied for more general object detection task with rotated Bboxes. To resolve this issue, we investigate the IoU computation for two rotated Bboxes first and then implement a unified framework, IoU loss layer for both 2D and 3D object detection tasks. By integrating the implemented IoU loss into several state-of-the-art 3D object detectors, consistent improvements have been achieved for both bird-eye-view 2D detection and point cloud 3D detection on the public KITTI [3] benchmark. Copyright © 2019, The Authors. All rights reserved.

关键词： Object recognition

来源：评论

学校读者我要写书评

暂无评论

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

arXiv

引用

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

关键词： Textures

来源：评论

学校读者我要写书评

暂无评论

A Spatio-Spectral Hybrid Convolutional Architecture for Hyperspectral Document Authentication

A Spatio-Spectral Hybrid Convolutional Architecture for Hype...

引用

International Conference on Document Analysis and Recognition

作者： Muhammad Jaleed Khan Khurram Khurshid Faisal Shafait Department of Electrical Engineering Institute of Space Technology (IST) Islamabad Pakistan School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan Deep Learning Laboratory National Center of Artificial Intelligence (NCAI) Islamabad Pakistan

Hyperspectral Document Image (HSDI) analysis allows for efficient and accurate differentiation of inks with visually similar color but unique spectral response, which is a crucial step in authentication of documents. Various HSDI based ink discrimination methods are available in the current literature, however, more accurate and robust methods are required to empower document authentication. Contrary to the former ink mismatch detection methods based on spectral features only, we present a novel method based on deep learning that exploits the spectral correlation as well as the spatial context to enhance ink mismatch detection. Spectral responses of the target pixel and its neighboring pixels are organized in an image format and fed to a Convolutional Neural Network (CNN) for classification. The proposed method achieves the highest accuracy among the other ink mismatch detection methods on the UWA Writing Ink Hyperspectral Images database (WIHSI), which demonstrates the effectiveness of deep learning models employing spatio-spectral hybrid features for document authentication. Detailed experimental analysis for selection of appropriate CNN architecture, spatio-spectral data format and training ratio is presented along with a comparison with the previous methods on this subject.

关键词： Ink Hyperspectral imaging Feature extraction Authentication deep learning Databases Text analysis

来源：评论

学校读者我要写书评

暂无评论

IDARTS: Interactive Differentiable Architecture Search

IDARTS: Interactive Differentiable Architecture Search

引用

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

来源：评论

学校读者我要写书评

暂无评论

CASIA-SURF: A Large-scale Multi-modal Benchmark for Face Anti-spoofing

arXiv

引用

arXiv 2019年

作者： Zhang, Shifeng Liu, Ajian Wan, Jun Liang, Yanyan Guo, Guogong Escalera, Sergio Escalante, Hugo Jair Li, Stan Z. National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Macau University of Science and Technology Macau China Institute of Deep Learning Baidu Research and National Engineering Laboratory for Deep Learning Technology and Application Universitat de Barcelona Computer Vision Center Barcelona Catalonia Instituto Nacional de Astrofsica Ptica y Electrnica Puebla72840 Mexico

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 antispoofing 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 CASIASURF, which is the largest publicly available dataset for face antispoofing 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 http://***/***/chalearnfacespoofingattackdete/. Copyright © 2019, The Authors. All rights reserved.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Out-of-town recommendation with travel intention modeling

arXiv

引用

arXiv 2021年

作者： Xin, Haoran Lu, Xinjiang Xu, Tong Liu, Hao Gu, Jingjing Dou, Dejing Xiong, Hui University of Science and Technology of China China Business Intelligence Lab Baidu Research China National Engineering Laboratory of Deep Learning Technology and Application China Nanjing University of Aeronautics and Astronautics China Rutgers University United States

Out-of-town recommendation is designed for those users who leave their home-town areas and visit the areas they have never been to before. It is challenging to recommend Point-of-Interests (POIs) for out-of-town users since the out-of-town check-in behavior is determined by not only the user’s home-town preference but also the user’s travel intention. Besides, the user’s travel intentions are complex and dynamic, which leads to big difficulties in understanding such intentions precisely. In this paper, we propose a TRAvel-INtention-aware Out-of-town Recommendation framework, named TRAINOR. The proposed TRAINOR framework distinguishes itself from existing out-of-town recommenders in three aspects. First, graph neural networks are explored to represent users’ home-town check-in preference and geographical constraints in out-of-town check-in behaviors. Second, a user-specific travel intention is formulated as an aggregation combining home-town preference and generic travel intention together, where the generic travel intention is regarded as a mixture of inherent intentions that can be learned by Neural Topic Model (NTM). Third, a non-linear mapping function, as well as a matrix factorization method, are employed to transfer users’ home-town preference and estimate out-of-town POI’s representation, respectively. Extensive experiments on real-world data sets validate the effectiveness of the TRAINOR framework. Moreover, the learned travel intention can deliver meaningful explanations for understanding a user’s travel purposes. © 2021, CC BY-NC-SA.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

LAE : Long-tailed Age Estimation

arXiv

引用

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

来源：评论

学校读者我要写书评

暂无评论

ProposalContrast: Unsupervised Pre-training for LiDAR-based 3D Object Detection

arXiv

引用

arXiv 2022年

作者： Yin, Junbo Zhou, Dingfu Zhang, Liangjun Fang, Jin Xu, Cheng-Zhong Shen, Jianbing Wang, Wenguan School of Computer Science Beijing Institute of Technology China Baidu Research China National Engineering Laboratory of Deep Learning Technology and Application China SKL-IOTSC CIS University of Macau China ReLER AAII University of Technology Sydney Australia

Existing approaches for unsupervised point cloud pre-training are constrained to either scene-level or point/voxel-level instance discrimination. Scene-level methods tend to lose local details that are crucial for recognizing the road objects, while point/voxel-level methods inherently suffer from limited receptive field that is incapable of perceiving large objects or context environments. Considering region-level representations are more suitable for 3D object detection, we devise a new unsupervised point cloud pre-training framework, called ProposalContrast, that learns robust 3D representations by contrasting region proposals. Specifically, with an exhaustive set of region proposals sampled from each point cloud, geometric point relations within each proposal are modeled for creating expressive proposal representations. To better accommodate 3D detection properties, ProposalContrast optimizes with both inter-cluster and inter-proposal separation, i.e., sharpening the discriminativeness of proposal representations across semantic classes and object instances. The generalizability and transferability of ProposalContrast are verified on various 3D detectors (i.e., PV-RCNN, CenterPoint, PointPillars and PointRCNN) and datasets (i.e., KITTI, Waymo and ONCE). Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Spatial object recommendation with hints: When spatial granularity matters

arXiv

引用

arXiv 2021年

作者： Luo, Hui Zhou, Jingbo Bao, Zhifeng Li, Shuangli Culpepper, J. Shane Ying, Haochao Liu, Hao Xiong, Hui RMIT University Australia Business Intelligence Lab Baidu Research National Engineering Laboratory of Deep Learning Technology and Application China University of Science and Technology of China China Zhejiang University China Rutgers University United States

Existing spatial object recommendation algorithms generally treat objects identically when ranking them. However, spatial objects often cover different levels of spatial granularity and thereby are heterogeneous. For example, one user may prefer to be recommended a region (say Manhattan), while another user might prefer a venue (say a restaurant). Even for the same user, preferences can change at different stages of data exploration. In this paper, we study how to support top-k spatial object recommendations at varying levels of spatial granularity, enabling spatial objects at varying granularity, such as a city, suburb, or building, as a Point of Interest (POI). To solve this problem, we propose the use of a POI tree, which captures spatial containment relationships between POIs. We design a novel multi-task learning model called MPR (short for Multi-level POI Recommendation), where each task aims to return the top-k POIs at a certain spatial granularity level. Each task consists of two subtasks: (i) attribute-based representation learning;(ii) interaction-based representation learning. The first subtask learns the feature representations for both users and POIs, capturing attributes directly from their profiles. The second subtask incorporates user-POI interactions into the model. Additionally, MPR can provide insights into why certain recommendations are being made to a user based on three types of hints: user-aspect, POI-aspect, and interaction-aspect. We empirically validate our approach using two real-life datasets, and show promising performance improvements over several state-of-the-art methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Forestry

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：