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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141-150 订阅

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What Object Should I Use? - Task Driven Object Detection 32

What Object Should I Use? - Task Driven Object Detection

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

作者： Sawatzky, Johann Souri, Yaser Grund, Christian Gall, Juergen Univ Bonn Bonn Germany

ISBN: (纸本)9781728132938

When humans have to solve everyday tasks, they simply pick the objects that are most suitable. While the question which object should one use for a specific task sounds trivial for humans, it is very difficult to answer for robots or other autonomous systems. This issue, however is not addressed by current benchmarks for object detection that focus on detecting object categories. We therefore introduce the COCO-Tasks dataset which comprises about 40,000 images where the most suitable objects for 14 tasks have been annotated. We further more propose an approach that detects the most suitable objects for a given task. The approach builds on a Gated Graph Neural Network to exploit the appearance of each object as well as the global context of all present objects in the scene. In our experiments, we show that the proposed approach outperforms other approaches that are evaluated on the dataset like classification or ranking approaches.

关键词： Categorization Datasets and Evaluation recognition: Detection Retrieval

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Learning Words by Drawing Images 32

Learning Words by Drawing Images

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

作者： Suris, Didac Recasens, Adria Bau, David Harwath, David Glass, James Torralba, Antonio MIT Cambridge MA 02139 USA

ISBN: (纸本)9781728132938

We propose a framework for learning through drawing. Our goal is to learn the correspondence between spoken words and abstract visual attributes, from a dataset of spoken descriptions of images. Building upon recent findings that GAN representations can be manipulated to edit semantic concepts in the generated output, we propose a new method to use such GAN-generated images to train a model using a triplet loss. To apply the method, we develop Audio CLEVRGAN, a new dataset of audio descriptions of GAN-generated CLEVR images, and we describe a training procedure that creates a curriculum of GAN-generated images that focuses training on image pairs that differ in a specific, informative way. Training is done without additional supervision beyond the spoken captions and the GAN. We find that training that takes advantage of GAN-generated edited examples results in improvements in the model's ability to learn attributes compared to previous results. Our proposed learning framework also results in models that can associate spoken words with some abstract visual concepts such as color and size.

关键词： vision + Language Visual Reasoning

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3D Local Features for Direct Pairwise Registration 32

3D Local Features for Direct Pairwise Registration

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

作者： Deng, Haowen Birdal, Tolga Ilic, Slobodan Tech Univ Munich Munich Germany Siemens AG Munich Germany Natl Univ Def Technol Changsha Hunan Peoples R China

ISBN: (纸本)9781728132938

We present a novel, data driven approach for solving the problem of registration of two point cloud scans. Our approach is direct in the sense that a single pair of corresponding local patches already provides the necessary transformation cue for the global registration. To achieve that, we first endow the state of the art PPF-FoldNet [18] auto-encoder (AE) with a pose-variant sibling, where the discrepancy between the two leads to pose-specific descriptors. Based upon this, we introduce RelativeNet, a relative pose estimation network to assign correspondence-specific orientations to the keypoints, eliminating any local reference frame computations. Finally, we devise a simple yet effective hypothesize-and-verify algorithm to quickly use the predictions and align two point sets. Our extensive quantitative and qualitative experiments suggests that our approach outperforms the state of the art in challenging real datasets of pairwise registration and that augmenting the keypoints with local pose information leads to better generalization and a dramatic speed-up.

关键词： 3D from Multiview and Sensors Categorization recognition: Detection Retrieval Robotics + Driving

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DeepCaps: Going Deeper with Capsule Networks 32

DeepCaps: Going Deeper with Capsule Networks

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

作者： Rajasegaran, Jathushan Jayasundara, Vinoj Jayasekara, Sandaru Jayasekara, Hirunima Seneviratne, Suranga Rodrigo, Ranga Univ Moratuwa Dept Elect & Telecommun Engn Moratuwa Sri Lanka Univ Sydney Sch Comp Sci Sydney NSW Australia

ISBN: (纸本)9781728132938

Capsule Network is a promising concept in deep learning, yet its true potential is not fully realized thus far, providing sub-par performance on several key benchmark datasets with complex data. Drawing intuition from the success achieved by Convolutional Neural Networks (CNNs) by going deeper, we introduce DeepCaps(1), a deep capsule network architecture which uses a novel 3D convolution based dynamic routing algorithm. With DeepCaps, we surpass the state-of-the-art results in the capsule network domain on CIFAR10, SVHN and Fashion MNIST, while achieving a 68% reduction in the number of parameters. Further, we propose a class-independent decoder network, which strengthens the use of reconstruction loss as a regularization term. This leads to an interesting property of the decoder, which allows us to identify and control the physical attributes of the images represented by the instantiation parameters.

关键词： Categorization Deep Learning recognition: Detection Retrieval

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Classification-Reconstruction Learning for Open-Set recognition 32

Classification-Reconstruction Learning for Open-Set Recognit...

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

作者： Yoshihashi, Ryota Shao, Wen Kawakami, Rei You, Shaodi Iida, Makoto Naemura, Takeshi Univ Tokyo Tokyo Japan CSIRO Data61 Canberra ACT Australia

ISBN: (纸本)9781728132938

Open-set classification is a problem of handling 'unknown' classes that are not contained in the training dataset, whereas traditional classifiers assume that only known classes appear in the test environment. Existing open-set classifiers rely on deep networks trained in a supervised manner on known classes in the training set;this causes specialization of learned representations to known classes and makes it hard to distinguish unknowns from knowns. In contrast, we train networks for joint classification and reconstruction of input data. This enhances the learned representation so as to preserve information useful for separating unknowns from knowns, as well as to discriminate classes of knowns. Our novel Classification-Reconstruction learning for Open-Set recognition (CROSR) utilizes latent representations for reconstruction and enables robust unknown detection without harming the known-class classification accuracy. Extensive experiments reveal that the proposed method outperforms existing deep open-set classifiers in multiple standard datasets and is robust to diverse outliers.

关键词： Deep Learning Representation Learning

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Unsupervised Domain-Specific Deblurring via Disentangled Representations 32

Unsupervised Domain-Specific Deblurring via Disentangled Rep...

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

作者： Lu, Boyu Chen, Jun-Cheng Chellappa, Rama Univ Maryland UMIACS College Pk MD 20742 USA

ISBN: (纸本)9781728132938

Image deblurring aims to restore the latent sharp images from the corresponding blurred ones. In this paper, we present an unsupervised method for domain-specific, single-image deblurring based on disentangled representations. The disentanglement is achieved by splitting the content and blur features in a blurred image using content encoders and blur encoders. We enforce a KL divergence loss to regularize the distribution range of extracted blur attributes such that little content information is contained. Meanwhile, to handle the unpaired training data, a blurring branch and the cycle-consistency loss are added to guarantee that the content structures of the deblurred results match the original images. We also add an adversarial loss on de blurred results to generate visually realistic images and a perceptual loss to further mitigate the artifacts. We perform extensive experiments on the tasks of face and text de blurring using both synthetic datasets and real images, and achieve improved results compared to recent state-of-the-art deblurring methods.

关键词： Deep Learning Low-level vision

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K-Nearest Neighbors Hashing 32

K-Nearest Neighbors Hashing

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

作者： He, Xiangyu Wang, Peisong Cheng, Jian Chinese Acad Sci Inst Automat NLPR Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Beijing Peoples R China

ISBN: (纸本)9781728132938

Hashing based approximate nearest neighbor search embeds high dimensional data to compact binary codes, which enables efficient similarity search and storage. However, the non-isometry sign(.) function makes it hard to project the nearest neighbors in continuous data space into the closest codewords in discrete Hamming space. In this work, we revisit the sign(.) function from the perspective of space partitioning. In specific, we bridge the gap between k-nearest neighbors and binary hashing codes with Shannon entropy. We further propose a novel K-Nearest Neighbors Hashing (KNNH) method to learn binary representations from KNN within the subspaces generated by sign(.). Theoretical and experimental results show that the KNN relation is of central importance to neighbor preserving embeddings, and the proposed method outperforms the stateof-the-arts on benchmark datasets.

关键词： Categorization recognition: Detection Retrieval

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Show, Control and Tell: A Framework for Generating Controllable and Grounded Captions 32

Show, Control and Tell: A Framework for Generating Controlla...

引用

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

作者： Cornia, Marcella Baraldi, Lorenzo Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy

ISBN: (纸本)9781728132938

Current captioning approaches can describe images using black-box architectures whose behavior is hardly controllable and explainable from the exterior. As an image can be described in infinite ways depending on the goal and the context at hand, a higher degree of controllability is needed to apply captioning algorithms in complex scenarios. In this paper, we introduce a novel framework for image captioning which can generate diverse descriptions by allowing both grounding and controllability. Given a control signal in the form of a sequence or set of image regions, we generate the corresponding caption through a recurrent architecture which predicts textual chunks explicitly grounded on regions, following the constraints of the given control. Experiments are conducted on Flickr30k Entities and on COCO Entities, an extended version of COCO in which we add grounding annotations collected in a semi-automatic manner. Results demonstrate that our method achieves state of the art performances on controllable image captioning, in terms of caption quality and diversity. Code and annotations are publicly available at: https://***/aimagelab/show-control-and-tell.

关键词： Deep Learning vision + Language Visual Reasoning

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A Late Fusion CNN for Digital Matting 32

A Late Fusion CNN for Digital Matting

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

作者： Zhang, Yunke Gong, Lixue Fan, Lubin Ren, Peiran Huang, Qixing Bao, Hujun Xu, Weiwei Zhejiang Univ Hangzhou Peoples R China Alibaba Grp Hangzhou Peoples R China Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781728132938

This paper studies the structure of a deep convolutional neural network to predict the foreground alpha matte by taking a single RGB image as input. Our network is fully convolutional with two decoder branches for the foreground and background classification respectively. Then a fusion branch is used to integrate the two classification results which gives rise to alpha values as the soft segmentation result. This design provides more degrees of freedom than a single decoder branch for the network to obtain better alpha values during training. The network can implicitly produce trimaps without user interaction, which is easy to use for novices without expertise in digital matting. Experimental results demonstrate that our network can achieve high-quality alpha mattes for various types of objects and outperform the state-of-the-art CNN-based image matting methods on the human image matting task.

关键词： Grouping and Shape Low-level vision Segmentation

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Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects 32

Sampling Techniques for Large-Scale Object Detection from Sp...

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

作者： Niitani, Yusuke Akiba, Takuya Kerola, Tommi Ogawa, Toru Sano, Shotaro Suzuki, Shuji Preferred Networks Inc Tokyo Japan

ISBN: (纸本)9781728132938

Efficient and reliable methods for training of object detectors are in higher demand than ever, and more and more data relevant to the field is becoming available. However, large datasets like Open Images Dataset v4 (OID) are sparsely annotated, and some measure must be taken in order to ensure the training of a reliable detector. In order to take the incompleteness of these datasets into account, one possibility is to use pretrained models to detect the presence of the unverified objects. However, the performance of such a strategy depends largely on the power of the pretrained model. In this study, we propose part-aware sampling, a method that uses human intuition for the hierarchical relation between objects. In terse terms, our method works by making assumptions like "a bounding box for a car should contain a bounding box for a tire". We demonstrate the power of our method on OID and compare the performance against a method based on a pretrained model. Our method also won the first and second place on the public and private test sets of the Google AI Open Images Competition 2018.

关键词： Categorization Deep Learning recognition: Detection Retrieval

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