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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461-470 订阅

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SPFTN: A Self-Paced Fine-Tuning Network for Segmenting Objects in Weakly Labelled Videos 30

SPFTN: A Self-Paced Fine-Tuning Network for Segmenting Objec...

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

作者： Zhang, Dingwen Yang, Le Meng, Deyu Xu, Dong Han, Junwei Northwestern Polytechincal Univ Xian Shaanxi Peoples R China Xi An Jiao Tong Univ Xian Shaanxi Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (纸本)9781538604571

Object segmentation in weakly labelled videos is an interesting yet challenging task, which aims at learning to perform category-specific video object segmentation by only using video-level tags. Existing works in this research area might still have some limitations, e.g., lack of effective DNN-based learning frameworks, under-exploring the context information, and requiring to leverage the unstable negative video collection, which prevent them from obtaining more promising performance. To this end, we propose a novel self-paced fine-tuning network (SPFTN)-based framework, which could learn to explore the context information within the video frames and capture adequate object semantics without using the negative videos. To perform weakly supervised learning based on the deep neural network, we make the earliest effort to integrate the self-paced learning regime and the deep neural network into a unified and compatible framework, leading to the self-paced fine-tuning network. Comprehensive experiments on the large-scale YouTube-Objects and DAVIS datasets demonstrate that the proposed approach achieves superior performance as compared with other state-of-the-art methods as well as the baseline networks and models.

关键词： Videos Training Object segmentation Image segmentation computer vision Neural networks Videotapes Object segmentation Neural network images computer vision image segmentation Functional training VIDEO FRAMES

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Feedback Networks 30

Feedback Networks

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

作者： Zamir, Amir R. Wu, Te-Lin Sun, Lin Shen, William B. Shi, Bertram E. Malik, Jitendra Savarese, Silvio Stanford Univ Stanford CA 94305 USA HKUST Hong Kong Hong Kong Peoples R China Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781538604571

Currently, the most successful learning models in computer vision are based on learning successive representations followed by a decision layer. this is usually actualized through feedforward multilayer neural networks, e.g. ConvNets, where each layer forms one of such successive representations. However, an alternative that can achieve the same goal is a feedback based approach in which the representation is formed in an iterative manner based on a feedback received from previous iteration's output. We establish that a feedback based approach has several core advantages over feedforward: it enables making early predictions at the query time, its output naturally conforms to a hierarchical structure in the label space (e.g. a taxonomy), and it provides a new basis for Curriculum Learning. We observe that feedback develops a considerably different representation compared to feedforward counterparts, in line with the aforementioned advantages. We provide a general feedback based learning architecture, instantiated using existing RNNs, with the endpoint results on par or better than existing feedforward networks and the addition of the above advantages.

关键词： Feedback label save area FEEDFORWARD search time feedforward network images computer vision hierarchical structure End Point representations

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Deep Quantization: Encoding Convolutional Activations with Deep Generative Model 30

Deep Quantization: Encoding Convolutional Activations with D...

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

作者： Qiu, Zhaofan Yao, Ting Mei, Tao Univ Sci & Technol China Hefei Anhui Peoples R China Microsoft Res Beijing Peoples R China

ISBN: (纸本)9781538604571

Deep convolutional neural networks (CNNs) have proven highly effective for visual recognition, where learning a universal representation from activations of convolutional layer plays a fundamental problem. In this paper, we present Fisher Vector encoding with Variational Auto-Encoder (FV-VAE), a novel deep architecture that quantizes the local activations of convolutional layer in a deep generative model, by training them in an end-to-end manner. To incorporate FV encoding strategy into deep generative models, we introduce Variational Auto-Encoder model, which steers a variational inference and learning in a neural network which can be straightforwardly optimized using standard stochastic gradient method. Different from the FV characterized by conventional generative models (e.g., Gaussian Mixture Model) which parsimoniously fit a discrete mixture model to data distribution, the proposed FV-VAE is more flexible to represent the natural property of data for better generalization. Extensive experiments are conducted on three public datasets, i.e., UCF101, ActivityNet, and CUB-200-2011 in the context of video action recognition and fine-grained image classification, respectively. Superior results are reported when compared to state-of-theart representations. Most remarkably, our proposed FVVAE achieves to-date the best published accuracy of 94.2% on UCF101.

关键词： Visualization Encoding computer architecture Training Quantization (signal) Computational modeling Optimization computer Architecture Computational modeling Visualization Functional training Variational Inference Training coding Neural network data distribution Image classification Mixture models Quantization

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Joint Registration and Representation Learning for Unconstrained Face Identification 30

Joint Registration and Representation Learning for Unconstra...

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

作者： Hayat, Munawar Khan, Salman H. Werghi, Naoufel Goecke, Roland Univ Canberra Canberra ACT Australia CSIRO Data61 Canberra ACT Australia Australian Natl Univ Canberra ACT Australia Khalifa Univ Abu Dhabi U Arab Emirates

ISBN: (纸本)9781538604571

Recent advances in deep learning have resulted in human-level performances on popular unconstrained face datasets including Labeled Faces in the Wild and YouTube Faces. To further advance research, IJB-A benchmark was recently introduced with more challenges especially in the form of extreme head poses. Registration of such faces is quite demanding and often requires laborious procedures like facial landmark localization. In this paper, we propose a Convolutional Neural Networks based data-driven approach which learns to simultaneously register and represent faces. We validate the proposed scheme on template based unconstrained face identification. Here, a template contains multiple media in the form of images and video frames. Unlike existing methods which synthesize all template media information at feature level, we propose to keep the template media intact. Instead, we represent gallery templates by their trained one-vs-rest discriminative models and then employ a Bayesian strategy which optimally fuses decisions of all medias in a query template. We demonstrate the efficacy of the proposed scheme on IJB-A, YouTube Celebrities and COX datasets where our approach achieves significant relative performance boosts of 3.6%, 21.6% and 12.8% respectively.

关键词： Facial recognition jigs(templates) Face YouTube Registration performance boost

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YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video 30

YouTube-BoundingBoxes: A Large High-Precision Human-Annotate...

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

作者： Real, Esteban Shlens, Jonathon Mazzocchi, Stefano Pan, Xin Vanhoucke, Vincent Google Brain Mountain View CA 94043 USA Google Res Mountain View CA 94043 USA

ISBN: (纸本)9781538604571

We introduce a new large-scale data set of video URLs with densely-sampled object bounding box annotations called YouTube-BoundingBoxes (YT-BB). the data set consists of approximately 380,000 video segments about 19s long, automatically selected to feature objects in natural settings without editing or post-processing, with a recording quality often akin to that of a hand-held cell phone camera. the objects represent a subset of the COCO [32] label set. All video segments were human-annotated with high-precision classification labels and bounding boxes at 1 frame per second. the use of a cascade of increasingly precise human annotations ensures a label accuracy above 95% for every class and tight bounding boxes. Finally, we train and evaluate well-known deep network architectures and report baseline figures for per-frame classification and localization to provide a point of comparison for future work. We also demonstrate how the temporal contiguity of video can potentially be used to improve such inferences. the data set can be found at https://***/youtube-bb. We hope the availability of such large curated corpus will spur new advances in video object detection and tracking.

关键词： YouTube Google Object detection Cameras Image segmentation computer vision Visualization Object detection Videotapes YouTube Cameras Dataset images computer vision image segmentation bounding boxes Visualization Google NETWORK STRUCTURE

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Instance-aware Image and Sentence Matching with Selective Multimodal LSTM 30

Instance-aware Image and Sentence Matching with Selective Mu...

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

作者： Huang, Yan Wang, Wei Wang, Liang Natl Lab Pattern Recognit Ctr Res Intelligent Percept & Comp CRIPAC Beijing Peoples R China Chinese Acad Sci CASIA Ctr Excellence Brain Sci & Intelligence Tech Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781538604571

Effective image and sentence matching depends on how to well measure their global visual-semantic similarity. Based on the observation that such a global similarity arises from a complex aggregation of multiple local similarities between pairwise instances of image (objects) and sentence (words), we propose a selective multimodal Long Short-Term Memory network (sm-LSTM) for instance-aware image and sentence matching. the sm-LSTM includes a multimodal context-modulated attention scheme at each timestep that can selectively attend to a pair of instances of image and sentence, by predicting pairwise instance-aware saliency maps for image and sentence. For selected pairwise instances, their representations are obtained based on the predicted saliency maps, and then compared to measure their local similarity. By similarly measuring multiple local similarities within a few timesteps, the sm-LSTM sequentially aggregates them with hidden states to obtain a final matching score as the desired global similarity. Extensive experiments show that our model can well match image and sentence with complex content, and achieve the state-of-the-art results on two public benchmark datasets.

关键词： Dogs Roads Image color analysis Aggregates Detectors pattern recognition

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Learning Detailed Face Reconstruction from a Single Image 30

Learning Detailed Face Reconstruction from a Single Image

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

作者： Richardson, Elad Sela, Matan Or-El, Roy Kimmel, Ron Technion Israel Inst Technol Dept Comp Sci Haifa Israel Univ Washington Dept Comp Sci & Engn Seattle WA 98195 USA

ISBN: (纸本)9781538604571

Reconstructing the detailed geometric structure of a face from a given image is a key to many computer vision and graphics applications, such as motion capture and reenactment. the reconstruction task is challenging as human faces vary extensively when considering expressions, poses, textures, and intrinsic geometries. While many approaches tackle this complexity by using additional data to reconstruct the face of a single subject, extracting facial surface from a single image remains a difficult problem. As a result, single-image based methods can usually provide only a rough estimate of the facial geometry. In contrast, we propose to leverage the power of convolutional neural networks to produce a highly detailed face reconstruction from a single image. For this purpose, we introduce an end-to-end CNN framework which derives the shape in a coarse-to-fine fashion. the proposed architecture is composed of two main blocks, a network that recovers the coarse facial geometry (CoarseNet), followed by a CNN that refines the facial features of that geometry (FineNet). the proposed networks are connected by a novel layer which renders a depth image given a mesh in 3D. Unlike object recognition and detection problems, there are no suitable datasets for training CNNs to perform face geometry reconstruction. therefore, our training regime begins with a supervised phase, based on synthetic images, followed by an unsupervised phase that uses only unconstrained facial images. the accuracy and robustness of the proposed model is demonstrated by both qualitative and quantitative evaluation tests.

关键词： Face Geometry Image reconstruction three-dimensional displays Solid modeling Training computer vision

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A Combinatorial Solution to Non-Rigid 3D Shape-to-Image Matching 30

A Combinatorial Solution to Non-Rigid 3D Shape-to-Image Matc...

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

作者： Bernard, Florian Schmidt, Frank R. thunberg, Johan Cremers, Daniel Univ Luxembourg Luxembourg Ctr Syst Biomed Luxembourg Luxembourg Ctr Hosp Luxembourg Luxembourg Luxembourg TUM Munich Germany

ISBN: (纸本)9781538604571

We propose a combinatorial solution for the problem of non-rigidly matching a 3D shape to 3D image data. To this end, we model the shape as a triangular mesh and allow each triangle of this mesh to be rigidly transformed to achieve a suitable matching to the image. By penalising the distance and the relative rotation between neighbouring triangles our matching compromises between image and shape information. In this paper, we resolve two major challenges: Firstly, we address the resulting large and NP-hard combinatorial problem with a suitable graph-theoretic approach. Secondly, we propose an efficient discretisation of the unbounded 6-dimensional Lie group SE(3). To our knowledge this is the first combinatorial formulation for non-rigid 3D shape-to-image matching. In contrast to existing local (gradient descent) optimisation methods, we obtain solutions that do not require a good initialisation and that are within a bound of the optimal solution. We evaluate the proposed method on the two problems of non-rigid 3D shape-to-shape and non-rigid 3D shape-to-image registration and demonstrate that it provides promising results.

关键词： Shape three-dimensional displays Two dimensional displays Optimization Computational modeling Labeling computer vision three-dimensional displays images Computational modeling computer vision image matching shape information Triangle triangular mesh Imagery (Psychotherapy)

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Convolutional neural network architecture for geometric matching 30

Convolutional neural network architecture for geometric matc...

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

作者： Rocco, Ignacio Arandjelovic, Relja Sivic, Josef DI ENS Paris France INRIA Villeneuve Dascq France CIIRC Prague Czech Republic PSL Res Univ ENS Dept Informat CNRS F-75005 Paris France Czech Tech Univ Czech Inst Informat Robot & Cybernet Prague Czech Republic DeepMind London England

ISBN: (纸本)9781538604571

We address the problem of determining correspondences between two images in agreement with a geometric model such as an affine or thin-plate spline transformation, and estimating its parameters. the contributions of this work are three-fold. First, we propose a convolutional neural network architecture for geometric matching. the architecture is based on three main components that mimic the standard steps of feature extraction, matching and simultaneous in-lier detection and model parameter estimation, while being trainable end-to-end. Second, we demonstrate that the network parameters can be trained from synthetically generated imagery without the need for manual annotation and that our matching layer significantly increases generalization capabilities to never seen before images. Finally, we show that the same model can perform both instance-level and category-level matching giving state-of-the-art results on the challenging Proposal Flow dataset.

关键词： Correlation Feature extraction computer architecture Estimation Geometry Robustness Neural networks

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Accurate Single Stage Detector Using Recurrent Rolling Convolution 30

Accurate Single Stage Detector Using Recurrent Rolling Convo...

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

作者： Ren, Jimmy Chen, Xiaohao Liu, Jianbo Sun, Wenxiu Pang, Jiahao Yan, Qiong Tai, Yu-Wing Xu, Li SenseTime Grp Ltd Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781538604571

Most of the recent successful methods in accurate object detection and localization used some variants of R-CNN style two stage Convolutional Neural Networks (CNN) where plausible regions were proposed in the first stage then followed by a second stage for decision refinement. Despite the simplicity of training and the efficiency in deployment, the single stage detection methods have not been as competitive when evaluated in benchmarks consider mAP for high IoU thresholds. In this paper, we proposed a novel single stage end-to-end trainable object detection network to overcome this limitation. We achieved this by introducing Recurrent Rolling Convolution (RRC) architecture over multi-scale feature maps to construct object classifiers and bounding box regressors which are "deep in context". We evaluated our method in the challenging KITTI dataset which measures methods under IoU threshold of 0.7. We showed that with RRC, a single reduced VGG-16 based model already significantly outperformed all the previously published results. At the time this paper was written our models ranked the first in KITTI car detection (the hard level), the first in cyclist detection and the second in pedestrian detection. these results were not reached by the previous single stage methods. the code is publicly available.

关键词： Feature extraction Proposals Detectors Convolution computer architecture Robustness Computational modeling

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