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检索条件"任意字段=31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018"

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Deep Diffeomorphic Transformer Networks 31

Deep Diffeomorphic Transformer Networks

引用

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

作者： Detlefsen, Nicki Skafte Freifeld, Oren Hauberg, Soren Tech Univ Denmark Lyngby Denmark Ben Gurion Univ Negev Beer Sheva Israel

ISBN: (纸本)9781538664209

Spatial Transformer layers allow neural networks, at least in principle, to be invariant to large spatial transformations in image data. The model has, however, seen limited uptake as most practical implementations support only transformations that are too restricted, e.g. affine or homographic maps, and/or destructive maps, such as thin plate splines. We investigate the use of flexible diffeomorphic image transformations within such networks and demonstrate that significant performance gains can be attained over currently-used models. The learned transformations are found to be both simple and intuitive, thereby providing insights into individual problem domains. With the proposed framework, a standard convolutional neural network matches state-of-the-art results on face verification with only two extra lines of simple TensorFlow code.

关键词： Face Neural networks computer architecture standards Task analysis Computational modeling Kernel

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Attentional ShapeContextNet for Point Cloud recognition 31

Attentional ShapeContextNet for Point Cloud Recognition

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

作者： Xie, Saining Liu, Sainan Chen, Zeyu Tu, Zhuowen Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (纸本)9781538664209

We tackle the problem of point cloud recognition. Unlike previous approaches where a point cloud is either converted into a volume/image or represented independently in a permutation-invariant set, we develop a new representation by adopting the concept of shape context as the building block in our network design. The resulting model, called ShapeContextNet, consists of a hierarchy with modules not relying on a fixed grid while still enjoying properties similar to those in convolutional neural networks being able to capture and propagate the object part information. In addition, we find inspiration from self-attention based models to include a simple yet effective contextual modeling mechanism making the contextual region selection, the feature aggregation, and the feature transformation process fully automatic. ShapeContextNet is an end-to-end model that can be applied to the general point cloud classification and segmentation problems. We observe competitive results on a number of benchmark datasets.

关键词： Shape Three-dimensional displays Kernel Two dimensional displays computer vision Neural networks

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Rethinking the Faster R-CNN Architecture for Temporal Action Localization 31

Rethinking the Faster R-CNN Architecture for Temporal Action...

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

作者： Chao, Yu-Wei Vijayanarasimhan, Sudheendra Seybold, Bryan Ross, David A. Deng, Jia Sukthankar, Rahul Univ Michigan Ann Arbor MI 48109 USA Google Res Pasadena CA USA

ISBN: (纸本)9781538664209

We propose TAL-Net, an improved approach to temporal action localization in video that is inspired by the Faster R-CNN object detection framework. TAL-Net addresses three key shortcomings of existing approaches: (1) we improve receptive field alignment using a multi-scale architecture that can accommodate extreme variation in action durations;(2) we better exploit the temporal context of actions for both proposal generation and action classification by appropriately extending receptive fields;and (3) we explicitly consider multi-stream feature fusion and demonstrate that fusing motion late is important. We achieve state-of-the-art performance for both action proposal and localization on THUMOS' 14 detection benchmark and competitive performance on ActivityNet challenge.

关键词： Proposals Object detection Feature extraction Two dimensional displays Microsoft Windows Image segmentation computer architecture

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Deformable GANs for Pose-based Human Image Generation 31

Deformable GANs for Pose-based Human Image Generation

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

作者： Siarohin, Aliaksandr Sangineto, Enver Lathuiliere, stephane Sebe, Nicu Univ Trento DISI Trento Italy Inria Grenoble Rhone Alpes Montbonnot St Martin France

ISBN: (纸本)9781538664209

In this paper we address the problem of generating person images conditioned on a given pose. Specifically, given an image of a person and a target pose, we synthesize a new image of that person in the novel pose. In order to deal with pixel-to-pixel misalignments caused by the pose differences, we introduce deformable skip connections in the generator of our Generative Adversarial Network. Moreover, a nearest-neighbour loss is proposed instead of the common L-1 and L-2 losses in order to match the details of the generated image with the target image. We test our approach using photos of persons in different poses and we compare our method with previous work in this area showing state-of-the-art results in two benchmarks. Our method can be applied to the wider field of deformable object generation, provided that the pose of the articulated object can be extracted using a keypoint detector

关键词： strain Generators Task analysis Gallium nitride Decoding computer architecture

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Aperture Supervision for Monocular Depth Estimation 31

Aperture Supervision for Monocular Depth Estimation

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

作者： Srinivasan, Pratul P. Garg, Rahul Wadhwa, Neal Ng, Ren Barron, Jonathan T. Univ Calif Berkeley Berkeley CA 94720 USA Google Res Mountain View CA USA

ISBN: (纸本)9781538664209

We present a novel method to train machine learning algorithms to estimate scene depths from a single image, by using the information provided by a camera's aperture as supervision. Prior works use a depth sensor's outputs or images of the same scene from alternate viewpoints as supervision, while our method instead uses images from the same viewpoint taken with a varying camera aperture. To enable learning algorithms to use aperture effects as supervision, we introduce two differentiable aperture rendering functions that use the input image and predicted depths to simulate the depth-of-field effects caused by real camera apertures. We train a monocular depth estimation network end-to-end to predict the scene depths that best explain these finite aperture images as defocus-blurred renderings of the input all-in-focus image.

关键词： Apertures Cameras Rendering (computer graphics) Estimation Geometry Prediction algorithms Three-dimensional displays

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PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place recognition 31

PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Sca...

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

作者： Uy, Mikaela Angelina Lee, Gim Hee Natl Univ Singapore Dept Comp Sci Singapore Singapore

ISBN: (纸本)9781538664209

Unlike its image based counterpart, point cloud based retrieval for place recognition has remained as an unexplored and unsolved problem. This is largely due to the difficulty in extracting local feature descriptors from a point cloud that can subsequently be encoded into a global descriptor for the retrieval task. In this paper, we propose the PointNetVLAD where we leverage on the recent success of deep networks to solve point cloud based retrieval for place recognition. Specifically, our PointNetVLAD is a combination/modification of the existing PointNet and NetVLAD, which allows end-to-end training and inference to extract the global descriptor from a given 3D point cloud. Furthermore, we propose the "lazy triplet and quadruplet" loss functions that can achieve more discriminative and generalizable global descriptors to tackle the retrieval task. We create benchmark datasets for point cloud based retrieval for place recognition, and the experimental results on these datasets show the feasibility of our PointNetVLAD. Our code and datasets are publicly available on the project web site (1).

关键词： Three-dimensional displays Laser radar Feature extraction Image recognition Databases Task analysis Two dimensional displays

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Blind Predicting Similar Quality Map for Image Quality Assessment 31

Blind Predicting Similar Quality Map for Image Quality Asses...

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

作者： Pan, Da Shi, Ping Hou, Ming Ying, Zefeng Fu, Sizhe Zhang, Yuan Commun Univ China 1 Dingfuzhuang East St Chaoyang Dist Beijing Peoples R China

ISBN: (纸本)9781538664209

A key problem in blind image quality assessment (BIQA) is how to effectively model the properties of human visual system in a data-driven manner. In this paper, we propose a simple and efficient BIQA model based on a novel framework which consists of a fully convolutional neural network (FCNN) and a pooling network to solve this problem. In principle, FCNN is capable of predicting a pixel-by-pixel similar quality map only from a distorted image by using the intermediate similarity maps derived from conventional full-reference image quality assessment methods. The predicted pixel-by-pixel quality maps have good consistency with the distortion correlations between the reference and distorted images. Finally, a deep pooling network regresses the quality map into a score. Experiments have demonstrated that our predictions outperform many state-of-the-art BIQA methods.

关键词： Image quality Distortion Indexes Predictive models Feature extraction Degradation Convolutional neural networks

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A Closer Look at Spatiotemporal Convolutions for Action recognition 31

A Closer Look at Spatiotemporal Convolutions for Action Reco...

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

作者： Tran, Du Wang, Heng Torresani, Lorenzo Ray, Jamie LeCun, Yann Paluri, Manohar Facebook Res Menlo Pk CA 94025 USA Dartmouth Coll Hanover NH 03755 USA

ISBN: (纸本)9781538664209

In this paper we discuss several forms of spatiotemporal convolutions for video analysis and study their effects on action recognition. Our motivation stems from the observation that 2D CNNs applied to individual frames of the video have remained solid performers in action recognition. In this work we empirically demonstrate the accuracy advantages of 3D CNNs over 2D CNNs within the framework of residual learning. Furthermore, we show that factorizing the 3D convolutional filters into separate spatial and temporal components yields significantly gains in accuracy. Our empirical study leads to the design of a new spatiotemporal convolutional block "R(2+1) D" which produces CNNs that achieve results comparable or superior to the state-of-the-art on Sports-1M, Kinetics, UCF101, and HMDB51.

关键词： Three-dimensional displays Two dimensional displays Spatiotemporal phenomena Solid modeling Feature extraction computer architecture

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Unsupervised Correlation Analysis 31

Unsupervised Correlation Analysis

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

作者： Hoshen, Yedid Wolf, Lior Facebook AI Res Menlo Pk CA 94025 USA Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9781538664209

Linking between two data sources is a basic building block in numerous computer vision problems. In this paper, we set to answer a fundamental cognitive question: are prior correspondences necessary for linking between different domains? One of the most popular methods for linking between domains is Canonical Correlation Analysis (CCA). All current CCA algorithms require correspondences between the views. We introduce a new method Unsupervised Correlation Analysis (UCA), which requires no prior correspondences between the two domains. The correlation maximization term in CCA is replaced by a combination of a reconstruction term (similar to autoencoders), full cycle loss, orthogonality and multiple domain confusion terms. Due to lack of supervision, the optimization leads to multiple alternative solutions with similar scores and we therefore introduce a consensus-based mechanism that is often able to recover the desired solution. Remarkably, this suffices in order to link remote domains such as text and images. We also present results on well accepted CCA benchmarks, showing that performance far exceeds other unsupervised baselines, and approaches supervised performance in some cases.

关键词： Correlation computer vision Task analysis Kernel Training Gallium nitride Facebook

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Object Referring in Videos with Language and Human Gaze 31

Object Referring in Videos with Language and Human Gaze

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

作者： Vasudevan, Arun Balajee Dai, Dengxin Van Gool, Luc Swiss Fed Inst Technol Zurich Switzerland Katholieke Univ Leuven Leuven Belgium

ISBN: (纸本)9781538664209

We investigate the problem of object referring (OR) i.e. to localize a target object in a visual scene coming with a language description. Humans perceive the world more as continued video snippets than as static images, and describe objects not only by their appearance, but also by their spatio-temporal context and motion features. Humans also gaze at the object when they issue a referring expression. Existing works for OR mostly focus on static images only, which fall short in providing many such cues. This paper addresses OR in videos with language and human gaze. To that end, we present a new video dataset for OR, with 30, 000 objects over 5, 000 stereo video sequences annotated for their descriptions and gaze. We further propose a novel network model for OR in videos, by integrating appearance, motion, gaze, and spatio-temporal context into one network. Experimental results show that our method effectively utilizes motion cues, human gaze, and spatio-temporal context. Our method outperforms previous OR methods. For dataset and code, please refer https//***/-arunv/***.

关键词： computer vision pattern recognition

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