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检索条件"任意字段=27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014"

共 227 条记录，以下是181-190 订阅

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Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups 30

Deep Roots: Improving CNN Efficiency with Hierarchical Filte...

引用

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

作者： Ioannou, Yani Robertson, Duncan Cipolla, Roberto Criminisi, Antonio Univ Cambridge Cambridge England Microsoft Res Washington DC USA

ISBN: (纸本)9781538604571

We propose a new method for creating computationally efficient and compact convolutional neural networks (CNNs) using a novel sparse connection structure that resembles a tree root. this allows a significant reduction in computational cost and number of parameters compared to state-of-the-art deep CNNs, without compromising accuracy, by exploiting the sparsity of inter-layer filter dependencies. We validate our approach by using it to train more efficient variants of state-of-the-art CNN architectures, evaluated on the CIFAR10 and ILSVRC datasets. Our results show similar or higher accuracy than the baseline architectures with much less computation, as measured by CPU and GPU timings. For example, for ResNet 50, our model has 40% fewer parameters, 45% fewer floating point operations, and is 31% (12%) faster on a CPU (GPU). For the deeper ResNet 200 our model has 48% fewer parameters and 27% fewer floating point operations, while maintaining state-of-the-art accuracy. For GoogLeNet, our model has 7% fewer parameters and is 21% (16%) faster on a CPU (GPU).

关键词： Computational modeling Convolution computer architecture Training Graphics processing units Computational complexity Neural networks

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Self-supervised learning of visual features through embedding images into text topic spaces 30

Self-supervised learning of visual features through embeddin...

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

作者： Gomez, Lluis Patel, Yash Rusinol, Marcal Karatzas, Dimosthenis Jawahar, C. V. UAB Comp Vis Ctr Barcelona Spain IIIT Hyderabad KCIS CVIT Hyderabad Andhra Prades India

ISBN: (纸本)9781538604571

End-to-end training from scratch of current deep architectures for new computer vision problems would require Imagenet-scale datasets, and this is not always possible. In this paper we present a method that is able to take advantage of freely available multi-modal content to train computer vision algorithms without human supervision. We put forward the idea of performing self-supervised learning of visual features by mining a large scale corpus of multimodal ( text and image) documents. We show that discriminative visual features can be learnt efficiently by training a CNN to predict the semantic context in which a particular image is more probable to appear as an illustration. For this we leverage the hidden semantic structures discovered in the text corpus with a well-known topic modeling technique. Our experiments demonstrate state of the art performance in image classification, object detection, and multimodal retrieval compared to recent self-supervised or natural-supervised approaches.

关键词： Visualization Semantics computer vision Training Encyclopedias Correlation

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Generalized Autoencoder: A Neural Network Framework for Dimensionality Reduction

Generalized Autoencoder: A Neural Network Framework for Dime...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Wang, Wei Huang, Yan Wang, Yizhou Wang, Liang Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit CRIPAC Beijing Peoples R China Peking Univ Sch EECS Natl Eng Lab Video Technol Key Lab Machine Percep Beijing Peoples R China

ISBN: (纸本)9781479943098

the autoencoder algorithm and its deep version as traditional dimensionality reduction methods have achieved great success via the powerful representability of neural networks. However, they just use each instance to reconstruct itself and ignore to explicitly model the data relation so as to discover the underlying effective manifold structure. In this paper, we propose a dimensionality reduction method by manifold learning, which iteratively explores data relation and use the relation to pursue the manifold structure. the method is realized by a so called "generalized autoencoder" (GAE), which extends the traditional autoencoder in two aspects: (1) each instance x(i) is used to reconstruct a set of instances {x(j)} rather than itself. (2) the reconstruction error of each instance (|parallel to x(j) - x(i)'parallel to(2)) is weighted by a relational function of x(i) and x(j) defined on the learned manifold. Hence, the GAE captures the structure of the data space through minimizing the weighted distances between reconstructed instances and the original ones. the generalized autoencoder provides a general neural network framework for dimensionality reduction. In addition, we propose a multilayer architecture of the generalized autoencoder called deep generalized autoencoder to handle highly complex datasets. Finally, to evaluate the proposed methods, we perform extensive experiments on three datasets. the experiments demonstrate that the proposed methods achieve promising performance.

关键词： Autoencoder Deep learning Dimensionality reduction

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Perceptual Organization and recognition of Indoor Scenes from RGB-D Images

Perceptual Organization and Recognition of Indoor Scenes fro...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Gupta, Saurabh Arbelaez, Pablo Malik, Jitendra Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9780769549897

We address the problems of contour detection, bottom-up grouping and semantic segmentation using RGB-D data. We focus on the challenging setting of cluttered indoor scenes, and evaluate our approach on the recently introduced NYU-Depth V2 (NYUD2) dataset [27]. We propose algorithms for object boundary detection and hierarchical segmentation that generalize the gPb - ucm approach of [2] by making effective use of depth information. We show that our system can label each contour with its type (depth, normal or albedo). We also propose a generic method for long-range amodal completion of surfaces and show its effectiveness in grouping. We then turn to the problem of semantic segmentation and propose a simple approach that classifies superpixels into the 40 dominant object categories in NYUD2. We use both generic and class-specific features to encode the appearance and geometry of objects. We also show how our approach can be used for scene classification, and how this contextual information in turn improves object recognition. In all of these tasks, we report significant improvements over the state-of-the-art.

关键词： feature extraction geometry image classification image coding image colour analysis image resolution image segmentation object detection object recognition

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Superpixel-based Tracking-by-Segmentation using Markov Chains 30

Superpixel-based Tracking-by-Segmentation using Markov Chain...

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

作者： Yeo, Donghun Son, Jeany Han, Bohyung Han, Joon Hee POSTECH Dept Comp Sci & Engn Pohang South Korea

ISBN: (纸本)9781538604571

We propose a simple but effective tracking-by-segmentation algorithm using Absorbing Markov Chain (AMC) on superpixel segmentation, where target state is estimated by a combination of bottom-up and top-down approaches, and target segmentation is propagated to subsequent frames in a recursive manner. Our algorithm constructs a graph for AMC using the superpixels identified in two consecutive frames, where background superpixels in the previous frame correspond to absorbing vertices while all other superpixels create transient ones. the weight of each edge depends on the similarity of scores in the end superpixels, which are learned by support vector regression. Once graph construction is completed, target segmentation is estimated using the absorption time of each superpixel. the proposed tracking algorithm achieves substantially improved performance compared to the state-of-the-art segmentation-based tracking techniques in multiple challenging datasets.

关键词： Target tracking Transient analysis Absorption computer vision Markov processes Image segmentation Support vector machines

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Landmark Based Facial Component Reconstruction for recognition Across Pose

Landmark Based Facial Component Reconstruction for Recogniti...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Hsu, Gee-Sern Peng, Hsiao-Chia Chang, Kai-Hsiang Natl Taiwan Univ Sci & Technol Dept Mech Engn Taipei Taiwan

ISBN: (纸本)9781479943098

Different from previous 3D face modeling approaches that consider the whole facial area, the proposed method reconstructs 3D facial components for handling cross-pose recognition. It has two phases, component reconstruction and component-based recognition. In the reconstruction phase, we first extract four component regions, namely two eyes, nose and mouth, from each gallery face using the pose-invariant landmarks obtained by a modified version of a landmark detection algorithm. A 3D model of each component region is reconstructed using a constrained minimization scheme with a gender and ethnicity oriented 3D model as the reference. In the recognition phase, the pose of a given probe is determined by a set of landmarks which guides the rotation of the reconstructed components so that the reconstructed can be aligned to the probe components. the match is determined by the components instead of the whole faces so that different components can be considered at different poses. Experiments on the PIE and Multi-PIE databases show that the proposed component-based approach does not just outperform its holistic counterpart, but is also competitive to many contemporary methods.

关键词： face recognition face reconstruction 3D facial component

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SphereFace: Deep Hypersphere Embedding for Face recognition 30

SphereFace: Deep Hypersphere Embedding for Face Recognition

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

作者： Liu, Weiyang Wen, Yandong Yu, Zhiding Li, Ming Raj, Bhiksha Song, Le Georgia Inst Technol Atlanta GA 30332 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Sun Yat Sen Univ Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538604571

this paper addresses deep face recognition (FR) problem under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space. However, few existing algorithms can effectively achieve this criterion. To this end, we propose the angular softmax (A-Softmax) loss that enables convolutional neural networks (CNNs) to learn angularly discriminative features. Geometrically, A-Softmax loss can be viewed as imposing discriminative constraints on a hypersphere manifold, which intrinsically matches the prior that faces also lie on a manifold. Moreover, the size of angular margin can be quantitatively adjusted by a parameter m. We further derive specific m to approximate the ideal feature criterion. Extensive analysis and experiments on Labeled Face in the Wild (LFW), Youtube Faces (YTF) and MegaFace Challenge 1 show the superiority of A-Softmax loss in FR tasks.

关键词： Facial recognition Hypersphere Face Francium Manifolds embedding

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Procedural Generation of Videos to Train Deep Action recognition Networks 30

Procedural Generation of Videos to Train Deep Action Recogni...

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

作者： Roberto de Souza, Cesar Gaidon, Adrien Cabon, Yohann Manuel Lopez, Antonio Xerox Res Ctr Europe Comp Vis Grp Meylan France Toyota Res Inst Los Altos CA USA Univ Autonoma Barcelona Ctr Vis Comp Bellaterra Spain

ISBN: (纸本)9781538604571

Deep learning for human action recognition in videos is making significant progress, but is slowed down by its dependency on expensive manual labeling of large video collections. In this work, we investigate the generation of synthetic training data for action recognition, as it has recently shown promising results for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation and other computer graphics techniques of modern game engines. We generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for "Procedural Human Action Videos". It contains a total of 39, 982 videos, with more than 1, 000 examples for each action of 35 categories. Our approach is not limited to existing motion capture sequences, and we procedurally define 14 synthetic actions. We introduce a deep multi-task representation learning architecture to mix synthetic and real videos, even if the action categories differ. Our experiments on the UCF101 and HMDB51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance, significantly outperforming fine-tuning state-of-the-art unsupervised generative models of videos.

关键词： Videos Cameras Animation Games Training Machine learning Manuals

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Convex Global 3D Registration with Lagrangian Duality 30

Convex Global 3D Registration with Lagrangian Duality

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

作者： Briales, Jesus Gonzalez-Jimenez, Javier Univ Malaga MAPIR UMA Grp Malaga Spain

ISBN: (纸本)9781538604571

the registration of 3D models by a Euclidean transformation is a fundamental task at the core of many application in computer vision. this problem is non- convex due to the presence of rotational constraints, making traditional local optimization methods prone to getting stuck in local minima. this paper addresses finding the globally optimal transformation in various 3D registration problems by a unified formulation that integrates common geometric registration modalities (namely point-to-point, point-to-line and point-to-plane). this formulation renders the optimization problem independent of both the number and nature of the correspondences. the main novelty of our proposal is the introduction of a strengthened Lagrangian dual relaxation for this problem, which surpasses previous similar approaches [32] in effectiveness. In fact, even though with no theoretical guarantees, exhaustive empirical evaluation in both synthetic and real experiments always resulted on a tight relaxation that allowed to recover a guaranteed globally optimal solution by exploiting duality theory. thus, our approach allows for effectively solving the 3D registration with global optimality guarantees while running at a fraction of the time for the state-of-the-art alternative [34], based on a more computationally intensive Branch and Bound method.

关键词： Registration images computer vision duality theory Branch and bound methods Lagrangian Non-Convex three Dimensions point to point communication

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Key Point-Based Driver Activity recognition

Key Point-Based Driver Activity Recognition

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

作者： Vats, Arpita Anastasiu, David C. Santa Clara Univ Santa Clara CA 95053 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We present a key point-based activity recognition framework, built upon pre-trained human pose estimation and facial feature detection models. Our method extracts complex static and movement-based features from key frames in videos, which are used to predict a sequence of key-frame activities. Finally, a merge procedure is employed to identify robust activity segments while ignoring outlier frame activity predictions. We analyze the different components of our framework via a wide array of experiments and draw conclusions with regards to the utility of the model and ways it can be improved. Results show our model is competitive, taking the 11th place out of 27 teams submitting to Track 3 of the 2022 AI City Challenge.

关键词： computer vision conferences Urban areas Pose estimation Activity recognition Predictive models Feature extraction

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