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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR"

共 1569 条记录，以下是241-250 订阅

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GESTURE recognition USING SPATIOTEMPORAL DEFORMABLE CONVOLUTIONAL REPRESENTATION 26

GESTURE RECOGNITION USING SPATIOTEMPORAL DEFORMABLE CONVOLUT...

引用

26th ieee International conference on Image Processing (ICIP)

作者： Shi, Lei Zhang, Yifan Hu, Jing Cheng, Jian Lu, Hanqing Jiangxi Elect Power Co Power Res Inst State Gride Nanchang Jiangxi Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781538662496

Dynamic gesture recognition, which plays an essential role in human-computer interaction, has been widely investigated but not yet addressed. the interference of the varied and complex background makes the classifier easily be misguided due to the relatively smaller size of the hands and arms compared with the full scenes. In this paper, we address the problem by proposing a novel spatiotemporal deformable convolutional neural network for end-to-end learning. To eliminate the background interference, a light-weight spatiotemporal deformable convolution module is specially designed to augment the spatiotemporal sampling locations of 3D convolution by learning additional offsets according to the preceding feature map. the proposed method is evaluated on two challenging datasets, EgoGesture and Jester, and achieves the state-of-the-art performance on both of the two datasets. the code and trained models will be released for better communication and future work.

关键词： Gesture recognition 3D CNNs

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GENERATING ADVERSARIAL EXAMPLES BY MAKEUP ATTACKS ON FACE recognition

GENERATING ADVERSARIAL EXAMPLES BY MAKEUP ATTACKS ON FACE RE...

引用

26th ieee International conference on Image Processing (ICIP)

作者： Zhu, Zheng-An Lu, Yun-Zhong Chiang, Chen-Kuo Natl Chung Cheng Univ Dept Comp Sci & Informat Engn Minxiong Taiwan

ISBN: (纸本)9781538662496

Deep Learning models have been developed rapidly and achieved great success in computer vision and natural language processing. In this paper, we propose to generate adversarial examples to attack well-trained face recognition models by applying makeup effect to face images. It consists of two generative adversarial networks (GANs) based sub-networks, Makeup Transfer Sub-network and Adversarial Attack Sub-network. Makeup Transfer Sub-network transfers the non-makeup face images to makeup faces. Adversarial Attack Sub-networks hides attack information within makeup effect. the generated face images make the well-trained face recognition models misclassified as dodge attack or target attack. the experimental results demonstrate that our method can generate high-quality face makeup images and achieve higher error rates on various face recognition models compared to the existing attack methods.

关键词： Adversarial example attack generative adversarial networks deep neural networks face recognition

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A Hilbert Curve Based Representation of sEMG Signals for Gesture recognition 26

A Hilbert Curve Based Representation of sEMG Signals for Ges...

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26th International conference on Systems, Signals and Image Processing (IWSSIP)

作者： Tsinganos, Panagiotis Cornelis, Bruno Cornelis, Jan Jansen, Bart Skodras, Athanassios Univ Patras Dept Elect & Comp Engn Patras 26504 Greece Vrije Univ Brussel Dept Elect & Informat B-1050 Brussels Belgium Imec B-3001 Leuven Belgium

ISBN: (纸本)9781728132273

Deep learning (DL) has transformed the field of data analysis by dramatically improving the state of the art in various classification and prediction tasks, especially in the area of computer vision. In biomedical engineering, a lot of new work is directed towards surface electromyography (sEMG) based gesture recognition, often addressed as an image classification problem using Convolutional Neural Networks (CNN). In this paper, we utilize the Hilbert space-filling curve for the generation of image representations of sEMG signals that are then classified by CNN. the proposed method is evaluated on different network architectures and yields a classification improvement of more than 3 %.

关键词： classification CNN deep learning electromyography hand gesture recognition Hilbert curve sEMG

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SINGLE IMAGE DEPth ESTIMATION USING DEEP ADVERSARIAL TRAINING 26

SINGLE IMAGE DEPTH ESTIMATION USING DEEP ADVERSARIAL TRAININ...

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26th ieee International conference on Image Processing (ICIP)

作者： Hambarde, Praful Dudhane, Akshay Murala, Subrahmanyam IIT Ropar Comp Vis & Pattern Recognit Lab Rupnagar India

ISBN: (纸本)9781538662496

Scene understanding is an active area of research in computer vision that encompasses several different problems. the LiDARs and stereo depth sensor have their own restrictions such as light sensitiveness, power consumption and short-range [1]. In this paper, we propose a two-stream deep adversarial network for single image depth estimation in RGB images. For stream I network, we propose a novel encoder-decoder architecture using residual concepts to extract course-level depth features. Stream II network purely processes the information through the residual architecture for fine-level depth estimation. Also, we designed a feature map sharing architecture to share the learned feature maps of the decoder module of stream I. Sharing feature maps strengthen the residual learning to estimate the scene depth and increase the robustness of the proposed network. A benchmark NYU RGB-D v2 [2] database is used to evaluate the proposed network for single image depth estimation. Both qualitative and quantitative analysis has been carried out to analyze the effectiveness of the proposed network for scene depth prediction. Performance analysis shows that the proposed method outperforms other existing methods for single image depth estimation.

关键词： Scene depth deep learning adversarial training

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High-speed Tracking with Multi-kernel Correlation Filters 31

High-speed Tracking with Multi-kernel Correlation Filters

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31st ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Tang, Ming Yu, Bin Zhang, Fan Wang, Jinqiao National Lab of Pattern Recognition Institute of Automation CAS Beijing 100190 China School of Info. & Comm. Eng. Beijing University of Posts and Telecommunications

ISBN: (纸本)9781538664209

Correlation filter (CF) based trackers are currently ranked top in terms of their performances. Nevertheless, only some of them, such as KCF [26] and MKCF [48], are able to exploit the powerful discriminability of non-linear kernels. Although MKCF achieves more powerful discriminability than KCF through introducing multi-kernel learning (MKL) into KCF, its improvement over KCF is quite limited and its computational burden increases significantly in comparison with KCF. In this paper, we will introduce the MKL into KCF in a different way than MKCF. We reformulate the MKL version of CF objective function with its upper bound, alleviating the negative mutual interference of different kernels significantly. Our novel MKCF tracker, MKCFup, outperforms KCF and MKCF with large margins and can still work at very high fps. Extensive experiments on public data sets show that our method is superior to state-of-the-art algorithms for target objects of small move at very high speed.

关键词： Kernel Correlation Linear programming Target tracking Upper bound Training

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FOXNET: A MULTI-FACE ALIGNMENT MEthOD 26

FOXNET: A MULTI-FACE ALIGNMENT METHOD

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26th ieee International conference on Image Processing (ICIP)

作者： Wu, Yuxiang Cheng, Zehua Huang, Bin Chen, Yiming Zhu, Xinghui Wang, Weiyang Hunan Agr Univ AISA Res Changsha Hunan Peoples R China SnowCloud Ai Beijing Peoples R China

ISBN: (纸本)9781538662496

Multi-face alignment aims to identify geometry structures of multiple faces in an image, and its performance is essential for the many practical tasks, such as face recognition, face tracking, and face animation. In this work, we present a fast bottom-up multi-face alignment approach, which can simultaneously localize multi-person facial landmarks with high precision. In more detail, our bottom-up architecture maps the landmarks to the high-dimensional space with which landmarks of all faces are represented. By clustering the features belonging to the same face, our approach can align the multi-person facial landmarks synchronously. Extensive experiments show that our method can achieve high performance in the multi-face landmark alignment task while our model is extremely fast. Moreover, we propose a new multi-face dataset to compare the speed and precision of bottom-up face alignment method withtop-down methods. Our dataset is publicly available at(1).

关键词： Face Alignment computer vision Deep Learning Cluster

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MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features 31

MaskLab: Instance Segmentation by Refining Object Detection ...

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31st ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Chen, Liang-Chieh Hermans, Alexander Papandreou, George Schroff, Florian Wang, Peng Adam, Hartwig Google Inc Menlo Pk CA 94025 USA Rhein Westfal TH Aachen Aachen Germany Univ Calif Los Angeles Los Angeles CA 90024 USA

ISBN: (纸本)9781538664209

In this work, we tackle the problem of instance segmentation, the task of simultaneously solving object detection and semantic segmentation. Towards this goal, we present a model, called MaskLab, which produces three outputs: box detection, semantic segmentation, and direction prediction. Building on top of the Faster-RCNN object detector, the predicted boxes provide accurate localization of object instances. Within each region of interest, MaskLab performs foreground/background segmentation by combining semantic and direction prediction. Semantic segmentation assists the model in distinguishing between objects of different semantic classes including background, while the direction prediction, estimating each pixel's direction towards its corresponding center, allows separating instances of the same semantic class. Moreover, we explore the effect of incorporating recent successful methods from both segmentation and detection (e.g., atrous convolution and hypercolumn). Our proposed model is evaluated on the COCO instance segmentation benchmark and shows comparable performance with other state-of-art models.

关键词： Semantics Agriculture Image segmentation Object detection Convolution Feature extraction Encoding

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Reconstructing Spectral Images from RGB-Images using a Convolutional Neural Network 31

Reconstructing Spectral Images from RGB-Images using a Convo...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Stiebel, Tarek Koppers, Simon Seltsam, Philipp Merhof, Dorit Rhein Westfal TH Aachen Inst Imaging & Comp Vis Aachen Germany

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Recovering high-dimensional spectral images taken with spectrally low-dimensional camera systems, in the extreme case RGB-images, has been of great interest for a variety of applications. An accurate spectral reconstruction is typically required to either achieve a better color accuracy or to improve object recognition/classification tasks. Almost all published work to date aims at performing a mapping from individual camera signals towards the corresponding spectrum. However, it might be beneficial to consider not only single pixels, but also contextual information. Here, we propose a convolutional neural network architecture that learns a mapping from RGB- to spectral images. We trained the network on the largest hyper-spectral data set available to date [;'I and analyzed the influence of different error metrics as loss functions. An objective evaluation of the performance in comparison to state of the art spectral reconstruction techniques is given by participating in the NTIRE 2018 challenge on spectral reconstruction [1].

关键词： computer vision pattern recognition conferences

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Neural Sign Language Translation 31

Neural Sign Language Translation

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31st ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Camgoz, Necati Cihan Hadfield, Simon Koller, Oscar Ney, Hermann Bowden, Richard Univ Surrey Guildford Surrey England Rhein Westfal TH Aachen Aachen Germany

ISBN: (纸本)9781538664209

Sign Language recognition (SLR) has been an active research field for the last two decades. However most research to date has considered SLR as a naive gesture recognition problem. SLR seeks to recognize a sequence of continuous signs but neglects the underlying rich grammatical and linguistic structures of sign language that differ from spoken language. In contrast, we introduce the Sign Language Translation (SLT) problem. Here, the objective is to generate spoken language translations from sign language videos, taking into account the different word orders and grammar. We formalize SLT in the framework of Neural Machine Translation (NMT) for both end-to-end and pretrained settings (using expert knowledge). this allows us to jointly learn the spatial representations, the underlying language model, and the mapping between sign and spoken language. To evaluate the performance of Neural SLT we collected the first publicly available Continuous SLT dataset, RWth-PHOENIX-Weather 2014T(1). It provides spoken language translations and gloss level annotations for German Sign Language videos of weather broadcasts. Our dataset contains over .95M frames with >67K signs from a sign vocabulary of >1K and >99K words from a German vocabulary of >2.8K. We report quantitative and qualitative results for various SLT setups to underpin future research in this newly established field. the upper bound for translation performance is calculated at 19.26 BLEU-4, while our end-to-end frame-level and gloss-level tokenization networks were able to achieve 9.58 and 18.13 respectively.

关键词： Assistive technology Videos Gesture recognition Task analysis Decoding Linguistics

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Attentive Feedback Network for Boundary-Aware Salient Object Detection

Attentive Feedback Network for Boundary-Aware Salient Object...

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ieee/CVF conference on computer vision and pattern recognition

作者： Mengyang Feng Huchuan Lu Errui Ding Dalian University of Technology Department of Computer Vision Technology (VIS) Baidu Inc.

ISBN: (纸本)9781728132945

Recent deep learning based salient object detection methods achieve gratifying performance built upon Fully Convolutional Neural Networks (FCNs). However, most of them have suffered from the boundary challenge. the state-of-the-art methods employ feature aggregation technique and can precisely find out wherein the salient object, but they often fail to segment out the entire object with fine boundaries, especially those raised narrow stripes. So there is still a large room for improvement over the FCN based models. In this paper, we design the Attentive Feedback Modules (AFMs) to better explore the structure of objects. A Boundary-Enhanced Loss (BEL) is further employed for learning exquisite boundaries. Our proposed deep model produces satisfying results on the object boundaries and achieves state-of-the-art performance on five widely tested salient object detection benchmarks. the network is in a fully convolutional fashion running at a speed of 26 FPS and does not need any post-processing.

关键词： Object detection feedback network boundary layer Boundary value problems Grain boundaries

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