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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71-80 订阅

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Feature Space Perturbations Yield More Transferable Adversarial Examples 32

Feature Space Perturbations Yield More Transferable Adversar...

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

作者： Inkawhich, Nathan Wen, Wei Li, Hai (Helen) Chen, Yiran Duke Univ Elect & Comp Engn Dept Durham NC 27708 USA

ISBN: (纸本)9781728132938

Many recent works have shown that deep learning models are vulnerable to quasi-imperceptible input perturbations, yet practitioners cannot fully explain this behavior. This work describes a transfer-based blackbox targeted adversarial attack of deep feature space representations that also provides insights into cross-model class representations of deep CNNs. The attack is explicitly designed for transferability and drives feature space representation of a source image at layer L towards the representation of a target image at L. The attack yields highly transferable targeted examples, which outperform competition winning methods by over 30% in targeted attack metrics. We also show the choice of L to generate examples from is important, transferability characteristics are blackbox model agnostic, and indicate that well trained deep models have similar highly-abstract representations.

关键词： Deep Learning Others vision Applications and Systems

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Joint Representative Selection and Feature Learning: A Semi-Supervised Approach 32

Joint Representative Selection and Feature Learning: A Semi-...

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

作者： Wang, Suchen Meng, Jingjing Yuan, Junsong Tan, Yap-Peng Nanyang Technol Univ Singapore Singapore SUNY Buffalo Buffalo NY USA

ISBN: (纸本)9781728132938

In this papa;we propose a semi-supervised approach for representative selection, which finds a small set of representatives that can well summarize a large data collection. Given labeled source data and big unlabeled target data, we aim to find representatives in the target data, which can not only represent and associate data points belonging to each labeled category, but also discover novel categories in the target data, if any. To leverage labeled source data, we guide representative selection from labeled source to unlabeled target. We propose a joint optimization framework which alternately optimizes (1) representative selection in the target data and (2) discriminative feature learning from both the source and the target for better representative selection. Experiments on image and video datasets demonstrate that our proposed approach not only finds better representatives, but also can discover novel categories in the target data that are not in the source.

关键词： Optimization Methods Representation Learning Video Analytics vision Applications and Systems

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Learning to Calibrate Straight Lines for Fisheye Image Rectification 32

Learning to Calibrate Straight Lines for Fisheye Image Recti...

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

作者： Xue, Zhucun Xue, Nan Xia, Gui-Song Shen, Weiming Wuhan Univ CAPTAIN LIESMARS Wuhan 430079 Hubei Peoples R China

ISBN: (纸本)9781728132938

This paper presents a new deep-learning based method to simultaneously calibrate the intrinsic parameters of fisheye lens and rectify the distorted images. Assuming that the distorted lines generated by fisheye projection should be straight after rectification, we propose a novel deep neural network to impose explicit geometry constraints onto processes of the fisheye lens calibration and the distorted image rectification. In addition, considering the nonlinearity of distortion distribution in fisheye images, the proposed network fully exploits multi-scale perception to equalize the rectification effects on the whole image. To train and evaluate the proposed model, we also create a new large-scale dataset labeled with corresponding distortion parameters and well-annotated distorted lines. Compared with the state-of-the-art methods, our model achieves the best published rectification quality and the most accurate estimation of distortion parameters on a large set of synthetic and real fisheye images.

关键词： Computational Photography Low-level vision

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MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation 32

MS-TCN: Multi-Stage Temporal Convolutional Network for Actio...

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

作者： Abu Farha, Yazan Gall, Juergen Univ Bonn Bonn Germany

ISBN: (纸本)9781728132938

Temporally locating and classifying action segments in long untrimmed videos is of particular interest to many applications like surveillance and robotics. While traditional approaches follow a two-step pipeline, by generating frame-wise probabilities and then feeding them to high-level temporal models, recent approaches use temporal convolutions to directly classify the video frames. In this paper, we introduce a multi-stage architecture for the temporal action segmentation task. Each stage features a set of dilated temporal convolutions to generate an initial prediction that is refined by the next one. This architecture is trained using a combination of a classification loss and a proposed smoothing loss that penalizes over-segmentation errors. Extensive evaluation shows the effectiveness of the proposed model in capturing long-range dependencies and recognizing action segments. Our model achieves state-of-the-art results on three challenging datasets: 50Salads, Georgia Tech Egocentric Activities (GTEA), and the Breakfast dataset.

关键词： Action recognition

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Learning to Synthesize Motion Blur 32

Learning to Synthesize Motion Blur

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

作者： Brooks, Tim Barron, Jonathan T. Google Res Mountain View CA 94043 USA

ISBN: (纸本)9781728132938

We present a technique for synthesizing a motion blurred image from a pair of unblurred images captured in succession. To build this system we motivate and design a differentiable "line prediction" layer to be used as part of a neural network architecture, with which we can learn a system to regress from image pairs to motion blurred images that span the capture time of the input image pair. Training this model requires an abundance of data, and so we design and execute a strategy for using frame interpolation techniques to generate a large-scale synthetic dataset of motion blurred images and their respective inputs. We additionally capture a high quality test set of real motion blurred images, synthesized from slow motion videos, with which we evaluate our model against several baseline techniques that can be used to synthesize motion blur. Our model produces higher accuracy output than our baselines, and is several orders of magnitude faster than baselines with competitive accuracy.

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

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Engaging Image Captioning via Personality 32

Engaging Image Captioning via Personality

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

作者： Shuster, Kurt Humeau, Samuel Hu, Hexiang Bordes, Antoine Weston, Jason Facebook AI Res Menlo Pk CA 94025 USA

ISBN: (纸本)9781728132938

Standard image captioning tasks such as COCO and Flickr30k are factual, neutral in tone and (to a human) state the obvious (e.g., "a man playing a guitar"). While such tasks are useful to verify that a machine understands the content of an image, they are not engaging to humans as captions. With this in mind we define a new task, PERSONALITY-CAPTIONS, where the goal is to be as engaging to humans as possible by incorporating controllable style and personality traits. We collect and release a large dataset of 241,858 of such captions conditioned over 215 possible traits. We build models that combine existing work from (i) sentence representations [36] with Transformers trained on 1.7 billion dialogue examples;and (ii) image representations [32] with ResNets trained on 3.5 billion social media images. We obtain state-of-the-art performance on Flickr30k and COCO, and strong performance on our new task. Finally, online evaluations validate that our task and models are engaging to humans, with our best model close to human performance.

关键词： Datasets and Evaluation vision + Language

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Blind Geometric Distortion Correction on Images Through Deep Learning 32

Blind Geometric Distortion Correction on Images Through Deep...

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

作者： Li, Xiaoyu Zhang, Bo Sander, Pedro V. Liao, Jing Hong Kong Univ Sci & Technol Hong Kong Peoples R China City Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781728132938

We propose the first general framework to automatically correct different types of geometric distortion in a single input image. Our proposed method employs convolutional neural networks (CNNs) trained by using a large synthetic distortion dataset to predict the displacement field between distorted images and corrected images. A model fitting method uses the CNN output to estimate the distortion parameters, achieving a more accurate prediction. The final corrected image is generated based on the predicted flow using an efficient, high-quality resampling method. Experimental results demonstrate that our algorithm outperforms traditional correction methods, and allows for interesting applications such as distortion transfer, distortion exaggeration, and co-occurring distortion correction.

关键词： Categorization Computational Photography Deep Learning recognition: Detection Retrieval

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Learning Implicit Fields for Generative Shape Modeling 32

Learning Implicit Fields for Generative Shape Modeling

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

作者： Chen, Zhiqin Zhang, Hao Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781728132938

We advocate the use of implicit fields for learning generative models of shapes and introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving the visual quality of the generated shapes. An implicit field assigns a value to each point in 3D space, so that a shape can be extracted as an iso-surface. IM-NET is trained to perform this assignment by means of a binary classifier. Specifically, it takes a point coordinate, along with a feature vector encoding a shape, and outputs a value which indicates whether the point is outside the shape or not. By replacing conventional decoders by our implicit decoder for representation learning (via IM-AE) and shape generation (via IM-GAN), we demonstrate superior results for tasks such as generative shape modeling, interpolation, and single-view 3D reconstruction, particularly in terms of visual quality. Code and supplementary material are available at https://***/czq142857/implicit-decoder.

关键词： 3D from Single Image Deep Learning vision + Graphics

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Text2Scene: Generating Compositional Scenes from Textual Descriptions 32

Text2Scene: Generating Compositional Scenes from Textual Des...

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

作者： Tan, Fuwen Feng, Song Ordonez, Vicente Univ Virginia Charlottesville VA 22903 USA IBM Thomas J Watson Res Ctr Yorktown Hts NY USA

ISBN: (纸本)9781728132938

In this paper we propose Text2Scene, a model that generates various forms of compositional scene representations from natural language descriptions. Unlike recent works, our method does NOT use Generative Adversarial Networks (GANs). Text2Scene instead learns to sequentially generate objects and their attributes(location,size, appearance, etc) at every time step by attending to different parts of the input text and the current status of the generated scene. We show that under minor modifications, the proposed framework can handle the generation of different forms of scene representations,including cartoon-like scenes, object layouts corresponding to real images, and synthetic images. Our method is not only competitive when compared with state-of-the-art GAN-based methods using automatic metrics and superior based on human judgments but also has the advantage of producing interpretable results.

关键词： Image and Video Synthesis vision + Language

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Content-Aware Multi-Level Guidance for Interactive Instance Segmentation 32

Content-Aware Multi-Level Guidance for Interactive Instance ...

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

作者： Majumder, Soumajit Yao, Angela Univ Bonn Inst Comp Sci 2 Bonn Germany Natl Univ Singapore Sch Comp Singapore Singapore

ISBN: (纸本)9781728132938

In interactive instance segmentation, users give feedback to iteratively refine segmentation masks. The user-provided clicks are transformed into guidance maps which provide the network with necessary cues on the whereabouts of the object of interest. Guidance maps used in current systems are purely distance-based and are either too localized or non-informative. We propose a novel transformation of user clicks to generate content-aware guidance maps that leverage the hierarchical structural information present in an image. Using our guidance maps, even the most basic FCNs are able to outperform existing approaches that require state-of-the-art segmentation networks pre-trained on large scale segmentation datasets. We demonstrate the effectiveness of our proposed transformation strategy through comprehensive experimentation in which we significantly raise state-of-the-art on four standard interactive segmentation benchmarks.

关键词： Deep Learning Grouping and Shape Segmentation vision Applications and Systems

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