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检索条件"任意字段=32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019"

共 858 条记录，以下是61-70 订阅

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Learning to Explain with Complemental Examples 32

Learning to Explain with Complemental Examples

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Kanehira, Atsushi Harada, Tatsuya Preferred Networks Tokyo Japan Univ Tokyo Tokyo Japan RIKEN Wako Saitama Japan

ISBN: (纸本)9781728132938

This paper addresses the generation of explanations with visual examples. Given an input sample, we build a system that not only classifies it to a specific category, but also outputs linguistic explanations and a set of visual examples that render the decision interpretable. Focusing especially on the complementarity of the multimodal information, i.e., linguistic and visual examples, we attempt to achieve it by maximizing the interaction information, which provides a natural definition of complementarity from an information theoretical viewpoint. We propose a novel framework to generate complemental explanations, on which the joint distribution of the variables to explain, and those to be explained is parameterized by three different neural networks: predictor, linguistic explainer, and example selector. Explanation models are trained collaboratively to maximize the interaction information to ensure the generated explanation are complemental to each other for the target. The results of experiments conducted on several datasets demonstrate the effectiveness of the proposed method.

关键词： vision + Language vision Applications and Systems

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Joint Manifold Diffusion for Combining Predictions on Decoupled Observations 32

Joint Manifold Diffusion for Combining Predictions on Decoup...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Kim, Kwang In Chang, Hyung Jin UNIST Ulsan South Korea Univ Birmingham Birmingham W Midlands England

ISBN: (纸本)9781728132938

We present a new predictor combination algorithm that improves a given task predictor based on potentially relevant reference predictors. Existing approaches are limited in that, to discover the underlying task dependence, they either require known parametric forms of all predictors or access to a single fixed dataset on which all predictors are jointly evaluated. To overcome these limitations, we design a new non-parametric task dependence estimation procedure that automatically aligns evaluations of heterogeneous predictors across disjoint feature sets. Our algorithm is instantiated as a robust manifold diffusion process that jointly refines the estimated predictor alignments and the corresponding task dependence. We apply this algorithm to the relative attributes ranking problem and demonstrate that it not only broadens the application range of predictor combination approaches but also outperforms existing methods even when applied to classical predictor combination settings.

关键词： Categorization recognition: Detection Representation Learning Retrieval Statistical Learning vision Applications and

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Homomorphic Latent Space Interpolation for Unpaired Image-To-Image Translation 32

Homomorphic Latent Space Interpolation for Unpaired Image-To...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Chen, Ying-Cong Xu, Xiaogang Tian, Zhuotao Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China Tencent Youtu Lab Hong Kong Peoples R China

ISBN: (纸本)9781728132938

Generative adversarial networks have achieved great success in unpaired image-to-image translation. Cycle consistency allows modeling the relationship between two distinct domains without paired data. In this paper, we propose an alternative framework, as an extension of latent space interpolation, to consider the intermediate region between two domains during translation. It is based on the fact that in a flat and smooth latent space, there exist many paths that connect two sample points. Properly selecting paths makes it possible to change only certain image attributes, which is useful for generating intermediate images between the two domains. We also show that this framework can be applied to multi-domain and multi-modal translation. Extensive experiments manifest its generality and applicability to various tasks.

关键词： And Body Pose Face Gesture Image and Video Synthesis vision Applications and Systems

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Style Transfer by Relaxed Optimal Transport and Self-Similarity 32

Style Transfer by Relaxed Optimal Transport and Self-Similar...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Kolkin, Nicholas Salavon, Jason Shakhnarovich, Gregory Toyota Technol Inst Chicago Chicago IL 60637 USA Univ Chicago Chicago IL 60637 USA

ISBN: (纸本)9781728132938

The goal of style transfer algorithms is to render the content of one image using the style of another We propose Style Transfer by Relaxed Optimal Transport and Self-Similarity (STROTSS), a new optimization-based style transfer algorithm. We extend our method to allow user-specified point-to-point or region-to-region control over visual similarity between the style image and the output. Such guidance can be used to either achieve a particular visual effect or correct errors made by unconstrained style transfer In order to quantitatively compare our method to prior work, we conduct a large-scale user study designed to assess the style-content tradeoff across settings in style transfer algorithms. Our results indicate that for any desired level of content preservation, our method provides higher-quality stylization than prior work.

关键词： Image and Video Synthesis vision + Graphics

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Model-blind Video Denoising Via Frame-to-frame Training 32

Model-blind Video Denoising Via Frame-to-frame Training

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Ehret, Thibaud Davy, Axel Morel, Jean-Michel Facciolo, Gabriele Arias, Pablo Univ Paris Saclay CNRS ENS Cachan CMLA F-94235 Cachan France

ISBN: (纸本)9781728132938

Modeling the processing chain that has produced a video is a difficult reverse engineering task, even when the camera is available. This makes model based video processing a still more complex task. In this paper we propose a fully blind video denoising method, with two versions off-line and on-line. This is achieved by fine-tuning a pretrained AWGN denoising network to the video with a novel frame-to-frame training strategy. Our denoiser can be used without knowledge of the origin of the video or burst and the post-processing steps applied from the camera sensor. The on-line process only requires a couple of frames before achieving visually pleasing results for a wide range of perturbations. It nonetheless reaches state-of-the-art performance for standard Gaussian noise, and can be used offline with still better performance.

关键词： Deep Learning Low-level vision

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Semantic Image Synthesis with Spatially-Adaptive Normalization 32

Semantic Image Synthesis with Spatially-Adaptive Normalizati...

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32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Park, Taesung Liu, Ming-Yu Wang, Ting-Chun Zhu, Jun-Yan Univ Calif Berkeley Berkeley CA 94720 USA NVIDIA Santa Clara CA USA MIT CSAIL Cambridge MA USA

ISBN: (纸本)9781728132938

We propose spatially-adaptive normalization, a simple but effective layer for synthesizing photorealistic images given an input semantic layout. Previous methods directly feed the semantic layout as input to the deep network, which is then processed through stacks of convolution, normalization, and nonlinearity layers. We show that this is suboptimal as the normalization layers tend to "wash away" semantic information. To address the issue, we propose using the input layout. for modulating the activations in normalization layers through a spatially-adaptive,learned transformation. Experiments on several challenging datasets demonstrate the advantage of the proposed method over existing approaches, regarding both visual fidelity and align-ment with input layouts. Finally, our model allows user control over both semantic and style as synthesizing images.

关键词： Computational Photography Deep Learning Image and Video Synthesis vision + Graphics

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Context-Aware Visual Compatibility Prediction 32

Context-Aware Visual Compatibility Prediction

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Cucurull, Guillem Taslakian, Perouz Vazquez, David Element AI Montreal PQ Canada

ISBN: (纸本)9781728132938

How do we determine whether two or more clothing items are compatible or visually appealing? Part of the answer lies in understanding of visual aesthetics, and is biased by personal preferences shaped by social attitudes, time, and place. In this work we propose a method that predicts compatibility between two items based on their visual features,as well as their context. We define context as the products that are known to be compatible with each of these item. Our model is in contrast to other metric learning approaches that rely on pairwise comparisons between item features alone. We address the compatibility prediction problem using a graph neural network that learns to generate product embeddings conditioned on their context. We present results for two prediction tasks (fill in the blank and outfit compatibility) tested on two fashion datasets Polyvore and Fashion-Gen, and on a subset of the Amazon dataset;we achieve state of the art results when using context information and show how test performance improves as more context is used.

关键词： Deep Learning Representation Learning vision Applications and Systems

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3D Point Capsule Networks 32

3D Point Capsule Networks

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32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zhao, Yongheng Birdal, Tolga Deng, Haowen Tombari, Federico Tech Univ Munich Munich Germany Univ Padua Padua Italy Siemens AG Munich Germany

ISBN: (纸本)9781728132938

In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data. 3D capsule networks arise as a direct consequence of our unified formulation of the common 3D auto-encoders. The dynamic routing scheme [30] and the peculiar 2D latent space deployed by our capsule networks bring in improvements for several common point cloud-related tasks, such as object classification, object reconstruction and part segmentation as substantiated by our extensive evaluations. Moreover, it enables new applications such as part interpolation and replacement.

关键词： Categorization Deep Learning recognition: Detection Representation Learning Retrieval RGBD sensors and analytics Robotics

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FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery 32

FineGAN: Unsupervised Hierarchical Disentanglement for Fine-...

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32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Singh, Krishna Kumar Ojha, Utkarsh Lee, Yong Jae Univ Calif Davis Davis CA 95616 USA

ISBN: (纸本)9781728132938

We propose FineGAN, a novel unsupervised GAN framework, which disentangles the background, object shape, and object appearance to hierarchically generate images of fine-grained object categories. To disentangle the factors without supervision, our key idea is to use information theory to associate each factor to a latent code, and to condition the relationships between the codes in a specific way to induce the desired hierarchy. Through extensive experiments, we show that FineGAN achieves the desired disentanglement to generate realistic and diverse images belonging to fine-grained classes of birds, dogs, and cars. Using FineGAN's automatically learned features, we also cluster real images as a first attempt at solving the novel problem of unsupen iced fine-grained object category discovery. Our code/models/demo can be found at https://***/kkanshul/fingan

关键词： Categorization Image and Video Synthesis recognition: Detection Retrieval

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Regularizing Activation Distribution for Training Binarized Deep Networks 32

Regularizing Activation Distribution for Training Binarized ...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Ding, Ruizhou Chin, Ting-Wu Liu, Zeye Marculescu, Diana Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781728132938

Binarized Neural Networks (BNNs) can significantly reduce the inference latency and energy consumption in resource-constrained devices due to their pure-logical computation and fewer memory accesses. However, training BNNs is difficult since the activation flow encounters degeneration, saturation, and gradient mismatch problems. Prior work alleviates these issues by increasing activation bits and adding floating-point scaling factors, thereby sacrificing BNN's energy efficiency. In this paper, we propose to use distribution loss to explicitly regularize the activation flow, and develop a framework to systematically formulate the loss. Our experiments show that the distribution loss can consistently improve the accuracy of BNNs without losing their energy benefits. Moreover, equipped with the proposed regularization, BNN training is shown to be robust to the selection of hyper-parameters including optimizer and learning rate.

关键词： Deep Learning vision Applications and Systems

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