检索结果-内蒙古大学图书馆

A bi-level formulation for label noise learning with spectral cluster discovery 29

学校读者我要写书评

暂无评论

A bi-level formulation for label noise learning with spectra...

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Luo, Yijing Han, Bo Gong, Chen PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China Jiangsu Key Lab of Image Video Understanding for Social Security China Department of Computer Science Hong Kong Baptist University Hong Kong Department of Computing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9780999241165

Practically, we often face the dilemma that some of the examples for training a classifier are incorrectly labeled due to various subjective and objective factors. Although intensive efforts have been put to design classifiers that are robust to label noise, most of the previous methods have not fully utilized data distribution information. To address this issue, this paper introduces a bi-level learning paradigm termed "Spectral Cluster Discovery" (SCD) for combating with noisy labels. Namely, we simultaneously learn a robust classifier (Learning stage) by discovering the low-rank approximation to the ground-truth label matrix and learn an ideal affinity graph (Clustering stage). Specifically, we use the learned classifier to assign the examples with similar label to a mutual cluster. Based on the cluster membership, we use the learned affinity graph to explore the noisy examples based on the cluster membership. Both stages will reinforce each other iteratively. Experimental results on typical benchmark and real-world datasets verify the superiority of SCD to other label noise learning methods. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Iterative methods

CU-Net: Cascaded U-net with loss weighted sampling for brain tumor segmentation

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Liu, Hongying Shen, Xiongjie Shang, Fanhua Wang, Fei Key Lab of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University China Weill Cornell Medical School Cornell University NY United States

This paper proposes a novel cascaded U-Net for brain tumor segmentation. Inspired by the distinct hierarchical structure of brain tumor, we design a cascaded deep network framework, in which the whole tumor is segmented firstly and then the tumor internal substructures are further segmented. Considering that the increase of the network depth brought by cascade structures leads to a loss of accurate localization information in deeper layers, we construct many skip connections to link features at the same resolution and transmit detailed information from shallow layers to the deeper layers. Then we present a loss weighted sampling (LWS) scheme to eliminate the issue of imbalanced data during training the network. Experimental results on BraTS 2017 data show that our architecture framework outperforms the state-of-the-art segmentation algorithms, especially in terms of segmentation sensitivity. Copyright © 2019, The Authors. All rights reserved.

关键词： Brain

Global Spatio-Temporal Fusion-based Traffic Prediction Algorithm with Anomaly Aware

学校读者我要写书评

暂无评论

Global Spatio-Temporal Fusion-based Traffic Prediction Algor...

IEEE Conference on Global Communications (GLOBECOM)

作者： Chaoqun Liu Xuanpeng Li Chen Gong Guangyu Li PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security Nanjing University of Science and Technology Southeast University Nanjing China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Traffic prediction is an indispensable component of urban planning and traffic management. Achieving accurate traffic prediction hinges on the ability to capture the potential spatio-temporal relationships among road sensors. However, the majority of existing works focus on local short-term spatiotemporal correlations, failing to fully consider the interactions of different sensors in the long-term state. In addition, these works do not analyze the influences of anomalous factors, or have insufficient ability to extract personalized features of anomalous factors, which make them ineffectively capture their spatiotemporal influences on traffic prediction. To address the aforementioned issues, We propose a global spatio-temporal fusionbased traffic prediction algorithm that incorporates anomaly awareness. Initially, based on the designed anomaly detection network, we construct an efficient anomalous factors impacting module (AFIM), to evaluate the spatio-temporal impact of unexpected external events on traffic prediction. Furthermore, we propose a multi-scale spatio-temporal feature fusion module (MTSFFL) based on the transformer architecture, to obtain all possible both long and short term correlations among different sensors in a wide-area traffic environment for accurate prediction of traffic flow. Finally, experiments are implemented based on real-scenario public transportation datasets (PEMS04 and PEMS08) to demonstrate that our approach can achieve stateof-the-art performance.

关键词： Correlation Accuracy Roads Urban planning Sensor fusion Prediction algorithms Feature extraction Transformers Sensors Spatiotemporal phenomena

DSFD: Dual Shot Face Detector

学校读者我要写书评

暂无评论

DSFD: Dual Shot Face Detector

IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Jian Li Yabiao Wang Changan Wang Ying Tai Jianjun Qian Jian Yang Chengjie Wang Jilin Li Feiyue Huang PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science Youtu Lab

ISBN: (纸本)9781728132945

In this paper, we propose a novel face detection network with three novel contributions that address three key aspects of face detection, including better feature learning, progressive loss design and anchor assign based data augmentation, respectively. First, we propose a Feature Enhance Module (FEM) for enhancing the original feature maps to extend the single shot detector to dual shot detector. Second, we adopt Progressive Anchor Loss (PAL) computed by two different sets of anchors to effectively facilitate the features. Third, we use an Improved Anchor Matching (IAM) by integrating novel anchor assign strategy into data augmentation to provide better initialization for the regressor. Since these techniques are all related to the two-stream design, we name the proposed network as Dual Shot Face Detector (DSFD). Extensive experiments on popular benchmarks, WIDER FACE and FDDB, demonstrate the superiority of DSFD over the state-of-the-art face detectors.

关键词： Boat anchors Facial Recognition detector shot Face Dual Disease Predictive Factor

An Efficient Skip Link-based Traffic Prediction Algorithm with Multi-Scale Feature Extraction

学校读者我要写书评

暂无评论

An Efficient Skip Link-based Traffic Prediction Algorithm wi...

International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Chaoqun Liu Xuanpeng Li Guangyu Li Chen Gong PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security Nanjing University of Science and Technology Southeast University Nanjing China

ISBN: (数字)9798350358261

ISBN: (纸本)9798350358278

Traffic prediction is an important component in the development of intelligent Transportation Systems (ITS). To improve the accuracy of traffic prediction, many existing studies focus on combining recurrent neural networks and graph neural networks and achieve certain effects, but these works still suffer from some limitations on realizing rigorous long-term traffic foreseeing in highly dynamic spatio-temporal environments. To address this issue, we propose an efficient skip link-based traffic prediction algorithm with multiscale feature extraction (SKMSGCN). Specifically, we firstly design an available Spatio-temporal Traffic Feature Extraction module (STFE) based on the established information fusion scheme, to sufficiently obtain long-term traffic feature correlations. In addition, by means of memory networks, we propose a Critical traffic Feature Classification module (CFC), to obtain notable features from traffic sensor nodes with different attributions and further increase traffic prediction accuracy. Moreover, in order to efficiently process the input data of enhanced traffic features from CFC module and achieve the precise traffic foreseeing, we construct a long-term traffic prediction module based on graph convolutional networks and gate recurrent units. The extensive experimental results on two typical benchmark datasets firmly demonstrate the effectiveness of the proposed SKMSGCN in quantitative aspects.

关键词： Accuracy Recurrent neural networks Time series analysis Predictive models Logic gates Prediction algorithms Feature extraction

Learning the redundancy-free features for generalized zero-shot object recognition

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Han, Zongyan Fu, Zhenyong Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology

Zero-shot object recognition or zero-shot learning aims to transfer the object recognition ability among the semantically related categories, such as fine-grained animal or bird species. However, the images of different fine-grained objects tend to merely exhibit subtle differences in appearance, which will severely deteriorate zero-shot object recognition. To reduce the superfluous information in the fine-grained objects, in this paper, we propose to learn the redundancy-free features for generalized zero-shot learning. We achieve our motivation by projecting the original visual features into a new (redundancy-free) feature space and then restricting the statistical dependence between these two feature spaces. Furthermore, we require the projected features to keep and even strengthen the category relationship in the redundancy-free feature space. In this way, we can remove the redundant information from the visual features without losing the discriminative information. We extensively evaluate the performance on four benchmark datasets. The results show that our redundancy-free feature based generalized zero-shot learning (RFF-GZSL) approach can achieve competitive results compared with the state-of-the-arts. Copyright © 2020, The Authors. All rights reserved.

关键词： Object recognition

Progressive point cloud deconvolution generation network

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Hui, Le Xu, Rui Xie, Jin Qian, Jianjun Yang, Jian Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image Video Understanding for Social Security PCA Lab School of Computer Science and Engineering Nanjing University of Science and Technology China

In this paper, we propose an effective point cloud generation method, which can generate multi-resolution point clouds of the same shape from a latent vector. Specifically, we develop a novel progressive deconvolution network with the learning-based bilateral interpolation. The learning-based bilateral interpolation is performed in the spatial and feature spaces of point clouds so that local geometric structure information of point clouds can be exploited. Starting from the low-resolution point clouds, with the bilateral interpolation and max-pooling operations, the deconvolution network can progressively output high-resolution local and global feature maps. By concatenating different resolutions of local and global feature maps, we employ the multi-layer perceptron as the generation network to generate multi-resolution point clouds. In order to keep the shapes of different resolutions of point clouds consistent, we propose a shape-preserving adversarial loss to train the point cloud deconvolution generation network. Experimental results demonstrate the effectiveness of our proposed method. Copyright © 2020, The Authors. All rights reserved.

关键词： Interpolation

Garment Animation NeRF with Color Editing

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Wang, Renke Zhang, Meng Li, Jun Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China

Generating high-fidelity garment animations through traditional workflows, from modeling to rendering, is both tedious and expensive. These workflows often require repetitive steps in response to updates in character motion, rendering viewpoint changes, or appearance edits. Although recent neural rendering offers an efficient solution for computationally intensive processes, it struggles with rendering complex garment animations containing fine wrinkle details and realistic garment-and-body occlusions, while maintaining structural consistency across frames and dense view rendering. In this paper, we propose a novel approach to directly synthesize garment animations from body motion sequences without the need for an explicit garment proxy. Our approach infers garment dynamic features from body motion, providing a preliminary overview of garment structure. Simultaneously, we capture detailed features from synthesized reference images of the garment's front and back, generated by a pre-trained image model. These features are then used to construct a neural radiance field that renders the garment animation video. Additionally, our technique enables garment recoloring by decomposing its visual elements. We demonstrate the generalizability of our method across unseen body motions and camera views, ensuring detailed structural consistency. Furthermore, we showcase its applicability to color editing on both real and synthetic garment data. Compared to existing neural rendering techniques, our method exhibits qualitative and quantitative improvements in garment dynamics and wrinkle detail modeling. Code is available at https://***/wrk226/GarmentAnimationNeRF. Copyright © 2024, The Authors. All rights reserved.

关键词： Animation

Learning superpoint graph cut for 3D instance segmentation 22

学校读者我要写书评

暂无评论

Learning superpoint graph cut for 3D instance segmentation

Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Le Hui Linghua Tang Yaqi Shen Jin Xie Jian Yang PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9781713871088

3D instance segmentation is a challenging task due to the complex local geometric structures of objects in point clouds. In this paper, we propose a learning-based superpoint graph cut method that explicitly learns the local geometric structures of the point cloud for 3D instance segmentation. Specifically, we first oversegment the raw point clouds into superpoints and construct the superpoint graph. Then, we propose an edge score prediction network to predict the edge scores of the superpoint graph, where the similarity vectors of two adjacent nodes learned through cross-graph attention in the coordinate and feature spaces are used for regressing edge scores. By forcing two adjacent nodes of the same instance to be close to the instance center in the coordinate and feature spaces, we formulate a geometry-aware edge loss to train the edge score prediction network. Finally, we develop a superpoint graph cut network that employs the learned edge scores and the predicted semantic classes of nodes to generate instances, where bilateral graph attention is proposed to extract discriminative features on both the coordinate and feature spaces for predicting semantic labels and scores of instances. Extensive experiments on two challenging datasets, ScanNet v2 and S3DIS, show that our method achieves new state-of-the-art performance on 3D instance segmentation.

关键词：