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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501-510 订阅

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Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning 30

Designing Energy-Efficient Convolutional Neural Networks usi...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yang, Tien-Ju Chen, Yu-Hsin Sze, Vivienne MIT Cambridge MA 02139 USA

ISBN: (纸本)9781538604571

Deep convolutional neural networks (CNNs) are indispensable to state-of-the-art computer vision algorithms. However, they are still rarely deployed on battery-powered mobile devices, such as smartphones and wearable gadgets, where vision algorithms can enable many revolutionary real-world applications. the key limiting factor is the high energy consumption of CNN processing due to its high computational complexity. While there are many previous efforts that try to reduce the CNN model size or the amount of computation, we find that they do not necessarily result in lower energy consumption. therefore, these targets do not serve as a good metric for energy cost estimation. To close the gap between CNN design and energy consumption optimization, we propose an energy-aware pruning algorithm for CNNs that directly uses the energy consumption of a CNN to guide the pruning process. the energy estimation methodology uses parameters extrapolated from actual hardware measurements. the proposed layer-by-layer pruning algorithm also prunes more aggressively than previously proposed pruning methods by minimizing the error in the output feature maps instead of the filter weights. For each layer, the weights are first pruned and then locally fine-tuned with a closed-form least-square solution to quickly restore the accuracy. After all layers are pruned, the entire network is globally fine-tuned using back-propagation. With the proposed pruning method, the energy consumption of AlexNet and GoogLeNet is reduced by 3.7x and 1.6x, respectively, with less than 1% top-5 accuracy loss. We also show that reducing the number of target classes in AlexNet greatly decreases the number of weights, but has a limited impact on energy consumption.

关键词： Energy consumption Memory management Hardware Estimation Computational modeling Measurement Smart phones energy consumption Smartphone Store management computer hardware Computational modeling tree surgery high energy-consuming

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Neural Scene De-rendering 30

Neural Scene De-rendering

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wu, Jiajun Tenenbaum, Joshua B. Kohli, Pushmeet MIT CSAIL Cambridge MA 02139 USA Microsoft Res Bengaluru India

ISBN: (纸本)9781538604571

We study the problem of holistic scene understanding. We would like to obtain a compact, expressive, and interpretable representation of scenes that encodes information such as the number of objects and their categories, poses, positions, etc. Such a representation would allow us to reason about and even reconstruct or manipulate elements of the scene. Previous works have used encoder-decoder based neural architectures to learn image representations;however, representations obtained in this way are typically uninterpretable, or only explain a single object in the scene. In this work, we propose a new approach to learn an interpretable distributed representation of scenes. Our approach employs a deterministic rendering function as the decoder, mapping a naturally structured and disentangled scene description, which we named scene XML, to an image. By doing so, the encoder is forced to perform the inverse of the rendering operation (a.k.a. de-rendering) to transform an input image to the structured scene XML that the decoder used to produce the image. We use a object proposal based encoder that is trained by minimizing both the supervised prediction and the unsupervised reconstruction errors. Experiments demonstrate that our approach works well on scene de-rendering with two different graphics engines, and our learned representation can be easily adapted for a wide range of applications like image editing, inpainting, visual analogy-making, and image captioning.

关键词： Engines Image reconstruction Proposals XML Rendering (computer graphics) Decoding

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Multigrid Neural Architectures 30

Multigrid Neural Architectures

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Ke, Tsung-Wei Maire, Michael Yu, Stella X. Univ Calif Berkeley ICSI Berkeley CA 94720 USA TTI Chicago Chicago IL USA

ISBN: (纸本)9781538604571

We propose a multigrid extension of convolutional neural networks (CNNs). Rather than manipulating representations living on a single spatial grid, our network layers operate across scale space, on a pyramid of grids. they consume multigrid inputs and produce multigrid outputs;convolutional filters themselves have both within-scale and cross-scale extent. this aspect is distinct from simple multiscale designs, which only process the input at different scales. Viewed in terms of information flow, a multigrid network passes messages across a spatial pyramid. As a consequence, receptive field size grows exponentially with depth, facilitating rapid integration of context. Most critically, multigrid structure enables networks to learn internal attention and dynamic routing mechanisms, and use them to accomplish tasks on which modern CNNs fail. Experiments demonstrate wide-ranging performance advantages of multigrid. On CIFAR and ImageNet classification tasks, flipping from a single grid to multigrid within the standard CNN paradigm improves accuracy, while being compute and parameter efficient. Multigrid is independent of other architectural choices;we show synergy in combination with residual connections. Multigrid yields dramatic improvement on a synthetic semantic segmentation dataset. Most strikingly, relatively shallow multigrid networks can learn to directly perform spatial transformation tasks, where, in contrast, current CNNs fail. Together, our results suggest that continuous evolution of features on a multigrid pyramid is a more powerful alternative to existing CNN designs on a flat grid.

关键词： Standards Convolution Routing computer architecture Semantics Image segmentation

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Zero-Shot Action recognition with Error-Correcting Output Codes 30

Zero-Shot Action Recognition with Error-Correcting Output Co...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Qin, Jie Liu, Li Shao, Ling Shen, Fumin Ni, Bingbing Chen, Jiaxin Wang, Yunhong Beihang Univ Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing Peoples R China Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing Peoples R China ETH Comp Vis Lab Zurich Switzerland Malong Technol Co Ltd Shenzhen Peoples R China Univ East Anglia Norwich Norfolk England Univ Elect Sci & Technol China Chengdu Sichuan Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9781538604571

Recently, zero-shot action recognition (ZSAR) has emerged with the explosive growth of action categories. In this paper, we explore ZSAR from a novel perspective by adopting the Error-Correcting Output Codes (dubbed ZSECOC). Our ZSECOC equips the conventional ECOC with the additional capability of ZSAR, by addressing the domain shift problem. In particular, we learn discriminative ZSECOC for seen categories from both categorylevel semantics and intrinsic data structures. this procedure deals with domain shift implicitly by transferring the well-established correlations among seen categories to unseen ones. Moreover, a simple semantic transfer strategy is developed for explicitly transforming the learned embeddings of seen categories to better fit the underlying structure of unseen categories. As a consequence, our ZSECOC inherits the promising characteristics from ECOC as well as overcomes domain shift, making it more discriminative for ZSAR. We systematically evaluate ZSECOC on three realistic action benchmarks, i.e. Olympic Sports, HMDB51 and UCF101. the experimental results clearly show the superiority of ZSECOC over the state-of-the-art methods.

关键词： Category error correction Codes recognition (Psychology) Shift Semantics Domain Action

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Spatiotemporal Pyramid Network for Video Action recognition 30

Spatiotemporal Pyramid Network for Video Action Recognition

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wang, Yunbo Long, Mingsheng Wang, Jianmin Yu, Philip S. Tsinghua Univ MOE KLiss Beijing Peoples R China Tsinghua Univ TNList Beijing Peoples R China Tsinghua Univ NEL BDSS Beijing Peoples R China Tsinghua Univ Sch Software Beijing Peoples R China Univ Illinois Chicago IL 60680 USA

ISBN: (纸本)9781538604571

Two-stream convolutional networks have shown strong performance in video action recognition tasks. the key idea is to learn spatiotemporal features by fusing convolutional networks spatially and temporally. However, it remains unclear how to model the correlations between the spatial and temporal structures at multiple abstraction levels. First, the spatial stream tends to fail if two videos share similar backgrounds. Second, the temporal stream may be fooled if two actions resemble in short snippets, though appear to be distinct in the long term. We propose a novel spatiotemporal pyramid network to fuse the spatial and temporal features in a pyramid structure such that they can reinforce each other. From the architecture perspective, our network constitutes hierarchical fusion strategies which can be trained as a whole using a unified spatiotemporal loss. A series of ablation experiments support the importance of each fusion strategy. From the technical perspective, we introduce the spatiotemporal compact bilinear operator into video analysis tasks. this operator enables efficient training of bilinear fusion operations which can capture full interactions between the spatial and temporal features. Our final network achieves state-of-the-art results on standard video datasets.

关键词： Videotapes Pyramid Network bilinearity Fusion reactions FUSION fusion (melting) Fusion, biological recognition (Psychology)

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Discriminative Covariance Oriented Representation Learning for Face recognition with Image Sets 30

Discriminative Covariance Oriented Representation Learning f...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wang, Wen Wang, Ruiping Shan, Shiguang Chen, Xilin Chinese Acad Sci Key Lab Intelligent Informat Proc Inst Comp Technol Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Cooperat Medianet Innovat Ctr Beijing Peoples R China

ISBN: (纸本)9781538604571

For face recognition with image sets, while most existing works mainly focus on building robust set models with hand-crafted feature, it remains a research gap to learn better image representations which can closely match the subsequent image set modeling and classification. Taking sample covariance matrix as set model in the light of its recent promising success, we present a Discriminative Covariance oriented Representation Learning (DCRL) framework to bridge the above gap. the framework constructs a feature learning network (e.g. a CNN) to project the face images into a target representation space, and the network is trained towards the goal that the set covariance matrix calculated in the target space has maximum discriminative ability. To encode the discriminative ability of set covariance matrices, we elaborately design two different loss functions, which respectively lead to two different representation learning schemes, i.e., the Graph Embedding scheme and the Softmax Regression scheme. Both schemes optimize the whole network containing both image representation mapping and set model classification in a joint learning manner. the proposed method is extensively validated on three challenging and large scale databases for the task of face recognition with image sets, i.e., YouTube Celebrities, YouTube Face DB and Point-and-Shoot Challenge.

关键词： Facial recognition images Image image representation variance covariance matrix modeling light Imagery (Psychotherapy) image set LOSS FUNCTION Covariance YouTube SETTING Scheme Set (Psychology) Covariance matrix

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A Deep Regression Architecture with Two-Stage Re-initialization for High Performance Facial Landmark Detection 30

A Deep Regression Architecture with Two-Stage Re-initializat...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Lv, Jiangjing Shao, Xiaohu Xing, Junliang Cheng, Cheng Zhou, Xi Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China

ISBN: (纸本)9781538604571

Regression based facial landmark detection methods usually learns a series of regression functions to update the landmark positions from an initial estimation. Most of existing approaches focus on learning effective mapping functions with robust image features to improve performance. the approach to dealing with the initialization issue, however, receives relatively fewer attentions. In this paper, we present a deep regression architecture with two-stage re-initialization to explicitly deal with the initialization problem. At the global stage, given an image with a rough face detection result, the full face region is firstly re-initialized by a supervised spatial transformer network to a canonical shape state and then trained to regress a coarse landmark estimation. At the local stage, different face parts are further separately re-initialized to their own canonical shape states, followed by another regression subnetwork to get the final estimation. Our proposed deep architecture is trained from end to end and obtains promising results using different kinds of unstable initialization. It also achieves superior performances over many competing algorithms.

关键词： Face Shape computer architecture Robustness Detectors Machine learning

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Deep Supervision with Shape Concepts for Occlusion-Aware 3D Object Parsing 30

Deep Supervision with Shape Concepts for Occlusion-Aware 3D ...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Li, Chi Zia, M. Zeeshan Quoc-Huy Tran Yu, Xiang Hager, Gregory D. Chandraker, Manmohan Johns Hopkins Univ Baltimore MD 21218 USA NEC Labs Amer Princeton NJ USA Univ Calif San Diego San Diego CA USA

ISBN: (纸本)9781538604571

Monocular 3D object parsing is highly desirable in various scenarios including occlusion reasoning and holistic scene interpretation. We present a deep convolutional neural network (CNN) architecture to localize semantic parts in 2D image and 3D space while inferring their visibility states, given a single RGB image. Our key insight is to exploit domain knowledge to regularize the network by deeply supervising its hidden layers, in order to sequentially infer intermediate concepts associated with the final task. To acquire training data in desired quantities with ground truth 3D shape and relevant concepts, we render 3D object CAD models to generate large-scale synthetic data and simulate challenging occlusion configurations between objects. We train the network only on synthetic data and demonstrate state-of-the-art performances on real image benchmarks including an extended version of KITTI, PASCAL VOC, PAS-CAL3D+ and IKEA for 2D and 3D keypoint localization and instance segmentation. the empirical results substantiate the utility of our deep supervision scheme by demonstrating effective transfer of knowledge from synthetic data to real images, resulting in less overfitting compared to standard end-to-end training.

关键词： three-dimensional displays Shape Two dimensional displays Solid modeling Training Rendering (computer graphics)

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Modeling Sub-Event Dynamics in First-Person Action recognition 30

Modeling Sub-Event Dynamics in First-Person Action Recogniti...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zaki, Hasan F. M. Shafait, Faisal Mian, Ajmal Univ Western Australia Sch Comp Sci & Software Engn Nedlands WA Australia Natl Univ Sci & Technol Islamabad Pakistan Int Islamic Univ Malaysia Dept Mechatron Engn Kuala Lumpur Selangor Malaysia

ISBN: (纸本)9781538604571

First-person videos have unique characteristics such as heavy egocentric motion, strong preceding events, salient transitional activities and post-event impacts. Action recognition methods designed for third person videos may not optimally represent actions captured by first-person videos. We propose a method to represent the high level dynamics of sub-events in first-person videos by dynamically pooling features of sub-intervals of time series using a temporal feature pooling function. the sub-event dynamics are then temporally aligned to make a new series. To keep track of how the sub-event dynamics evolve over time, we recursively employ the Fast Fourier Transform on a pyramidal temporal structure. the Fourier coefficients of the segment define the overall video representation. We perform experiments on two existing benchmark first-person video datasets which have been captured in a controlled environment. Addressing this gap, we introduce a new dataset collected from YouTube which has a larger number of classes and a greater diversity of capture conditions thereby more closely depicting real-world challenges in first-person video analysis. We compare our method to state-of-the-art first person and generic video recognition algorithms. Our method consistently outperforms the nearest competitors by 10.3%, 3.3% and 11.7% respectively on the three datasets.

关键词： Videos Time series analysis Feature extraction Dynamics Neurons Cameras Observers

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Adversarial Discriminative Domain Adaptation 30

Adversarial Discriminative Domain Adaptation

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Tzeng, Eric Hoffman, Judy Saenko, Kate Darrell, Trevor Univ Calif Berkeley Berkeley CA 94720 USA Stanford Univ Stanford CA 94305 USA Boston Univ Boston MA 02215 USA

ISBN: (纸本)9781538604571

Adversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across diverse domains. they can also improve recognition despite the presence of domain shift or dataset bias: recent adversarial approaches to unsupervised domain adaptation reduce the difference between the training and test domain distributions and thus improve generalization performance. However, while generative adversarial networks (GANs) show compelling visualizations, they are not optimal on discriminative tasks and can be limited to smaller shifts. On the other hand, discriminative approaches can handle larger domain shifts, but impose tied weights on the model and do not exploit a GAN-based loss. In this work, we first outline a novel generalized framework for adversarial adaptation, which subsumes recent state-of-the-art approaches as special cases, and use this generalized view to better relate prior approaches. We then propose a previously unexplored instance of our general framework which combines discriminative modeling, untied weight sharing, and a GAN loss, which we call Adversarial Discriminative Domain Adaptation (ADDA). We show that ADDA is more effective yet considerably simpler than competing domainadversarial methods, and demonstrate the promise of our approach by exceeding state-of-the-art unsupervised adaptation results on standard domain adaptation tasks as well as a difficult cross-modality object classification task.

关键词： Domain Dataset recognition (Psychology)

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