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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"

共 320 条记录，以下是21-30 订阅

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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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Egocentric Activity recognition on a Budget 31

Egocentric Activity Recognition on a Budget

引用

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

作者： Possas, Rafael Caceres, Sheila Pinto Ramos, Fabio Univ Sydney Sch Informat Technol Sydney NSW Australia

ISBN: (纸本)9781538664209

Recent advances in embedded technology have enabled more pervasive machine learning. One of the common applications in this field is Egocentric Activity recognition (EAR), where users wearing a device such as a smartphone or smartglasses are able to receive feedback from the embedded device. Recent research on activity recognition has mainly focused on improving accuracy by using resource intensive techniques such as multi-stream deep networks. Although this approach has provided state-of-the-art results, in most cases it neglects the natural resource constraints (e.g. battery) of wearable devices. We develop a Reinforcement Learning model-free method to learn energy-aware policies that maximize the use of low-energy cost predictors while keeping competitive accuracy levels. Our results show that a policy trained on an egocentric dataset is able use the synergy between motion and vision sensors to effectively tradeoff energy expenditure and accuracy on smartglasses operating in realistic, real-world conditions.

关键词： Activity recognition Ear Videos Cameras Task analysis Optical imaging

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The Best of Both Worlds: Combining CNNs and Geometric Constraints for Hierarchical Motion Segmentation 31

The Best of Both Worlds: Combining CNNs and Geometric Constr...

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

作者： Bideau, Pia RoyChowdhury, Aruni Menon, Rakesh R. Learned-Miller, Erik Univ Massachusetts Amherst Coll Informat & Comp Sci Amherst MA 01003 USA

ISBN: (纸本)9781538664209

Traditional methods of motion segmentation use powerful geometric constraints to understand motion, but fail to leverage the semantics of high-level image understanding. Modern CNN methods of motion analysis, on the other hand, excel at identifying well-known structures, but may not precisely characterize well-known geometric constraints. In this work, we build a new statistical model of rigid motion flow based on classical perspective projection constraints. We then combine piecewise rigid motions into complex deformable and articulated objects, guided by semantic segmentation from CNNs and a second "object level" statistical model. This combination of classical geometric knowledge combined with the pattern recognition abilities of CNNs yields excellent performance on a wide range of motion segmentation benchmarks, from complex geometric scenes to camouflaged animals.

关键词： Motion segmentation computer vision Optical imaging Cameras Three-dimensional displays Semantics Animals

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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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Document Enhancement using Visibility Detection 31

Document Enhancement using Visibility Detection

引用

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

作者： Kligler, Netanel Katz, Sagi Tal, Ayellet Technion Haifa Israel

ISBN: (纸本)9781538664209

This paper re-visits classical problems in document enhancement. Rather than proposing a new algorithm for a specific problem, we introduce a novel general approach. The key idea is to modify any state-of-the-art algorithm, by providing it with new information (input), improving its own results. Interestingly, this information is based on a solution to a seemingly unrelated problem of visibility detection in R-3. We show that a simple representation of an image as a 3D point cloud, gives visibility detection on this cloud a new interpretation. What does it mean for a point to be visible? Although this question has been widely studied within computer vision, it has always been assumed that the point set is a sampling of a real scene. We show that the answer to this question in our context reveals unique and useful information about the image. We demonstrate the benefit of this idea for document binarization and for unshadowing.

关键词： Three-dimensional displays Task analysis computer vision Color Lighting Light sources Transforms

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Time-resolved Light Transport Decomposition for Thermal Photometric stereo 31

Time-resolved Light Transport Decomposition for Thermal Phot...

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

作者： Tanaka, Kenichiro Ikeya, Nobuhiro Takatani, Tsuyoshi Kubo, Hiroyuki Funatomi, Takuya Mukaigawa, Yasuhiro Nara Inst Sci & Technol NAIST Ikoma Japan

ISBN: (纸本)9781538664209

We present a novel time-resolved light transport decomposition method using thermal imaging. Because the speed of heat propagation is much slower than the speed of light propagation, transient transport of far infrared light can be observed at a video frame rate. A key observation is that the thermal image looks similar to the visible light image in an appropriately controlled environment. This implies that conventional computer vision techniques can be straight-forwardly applied to the thermal image. We show that the diffuse component in the thermal image can be separated and, therefore, the surface normals of objects can be estimated by the Lambertian photometric stereo. The effectiveness of our method is evaluated by conducting real-world experiments, and its applicability to black body, transparent, and translucent objects is shown.

关键词： Cameras Infrared heating computer vision Transient analysis Light sources Scattering

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Local and Global Optimization Techniques in Graph-based Clustering 31

Local and Global Optimization Techniques in Graph-based Clus...

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

作者： Ikami, Daiki Yamasaki, Toshihiko Aizawa, Kiyoharu Univ Tokyo Tokyo Japan

ISBN: (纸本)9781538664209

The goal of graph-based clustering is to divide a dataset into disjoint subsets with members similar to each other from an affinity (similarity) matrix between data. The most popular method of solving graph-based clustering is spectral clustering. However;spectral clustering has draw-backs. Spectral clustering can only be applied to macro-average-based cost functions, which tend to generate undesirable small clusters. This study first introduces a novel cost function based on micro-average. We propose a local optimization method, which is widely applicable to graph-based clustering cost functions. We also propose an initial-guess-free algorithm to avoid its initialization dependency. Moreover;we present two global optimization techniques. The experimental results exhibit significant clustering performances from our proposed methods, including 100% clustering accuracy in the COIL-20 dataset.

关键词： Cost function Optimization methods Linear programming Sparse matrices computer vision Clustering algorithms

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Deep Layer Aggregation 31

Deep Layer Aggregation

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

作者： Yu, Fisher Wang, Dequan Shelhamer, Evan Darrell, Trevor Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781538664209

Visual recognition requires rich representations that span levels from low to high, scales from small to large, and resolutions from fine to coarse. Even with the depth of features in a convolutional network, a layer in isolation is not enough: compounding and aggregating these representations improves inference of what and where. Architectural efforts are exploring many dimensions for network backbones, designing deeper or wider architectures, but how to best aggregate layers and blocks across a network deserves further attention. Although skip connections have been incorporated to combine layers, these connections have been "shallow" themselves, and only fuse by simple, one-step operations. We augment standard architectures with deeper aggregation to better fuse information across layers. Our deep layer aggregation structures iteratively and hierarchically merge the feature hierarchy to make networks with better accuracy and fewer parameters. Experiments across architectures and tasks show that deep layer aggregation improves recognition and resolution compared to existing branching and merging schemes.

关键词： Semantics Task analysis computer architecture Visualization Aggregates Spatial resolution Convolution

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Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge 31

Tips and Tricks for Visual Question Answering: Learnings fro...

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

作者： Teney, Damien Anderson, Peter He, Xiaodong van den Hengel, Anton Univ Adelaide Adelaide SA Australia Australian Natl Univ Canberra ACT Australia JD AI Res Beijing Peoples R China Microsoft Res Redmond WA USA

ISBN: (纸本)9781538664209

Deep Learning has had a transformative impact on computer vision, but for all of the success there is also a significant cost. This is that the models and procedures used are so complex and intertwined that it is often impossible to distinguish the impact of the individual design and engineering choices each model embodies. This ambiguity diverts progress in the field, and leads to a situation where developing a state-of-the-art model is as much an art as a science. As a step towards addressing this problem we present a massive exploration of the effects of the myriad architectural and hyperparameter choices that must be made in generating a state-of-the-art model. The model is of particular interest because it won the 2017 Visual Question Answering Challenge. We provide a detailed analysis of the impact of each choice on model performance, in the hope that it will inform others in developing models, but also that it might set a precedent that will accelerate scientific progress in the field.

关键词： Training Visualization Task analysis Neural networks Knowledge discovery Logic gates computer architecture

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Exploring Disentangled Feature Representation Beyond Face Identification 31

Exploring Disentangled Feature Representation Beyond Face Id...

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

作者： Liu, Yu Wei, Fangyin Shao, Jing Sheng, Lu Yan, Junjie Wang, Xiaogang Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China SenseTime Grp Ltd Hong Kong Peoples R China

ISBN: (纸本)9781538664209

This paper proposes learning disentangled but complementary face features with a minimal supervision by face identification. Specifically, we construct an identity Distilling and Dispelling Autoencoder (D2AE) framework that adversarially learns the identity-distilled features for identity verification and the identity-dispelled features to fool the verification system. Thanks to the design of two-stream cues, the learned disentangled features represent not only the identity or attribute but the complete input image. Comprehensive evaluations further demonstrate that the proposed features not only preserve state-of-the-art identity verification performance on LFW, but also acquire comparable discriminative power for face attribute recognition on CelebA and LFWA. Moreover, the proposed system is ready to semantically control the face generation/editing based on various identities and attributes in an unsupervised manner.

关键词： Face Feature extraction Task analysis Training Semantics Image reconstruction Face recognition

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