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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81-90 订阅

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Convolutional Recurrent Network for Road Boundary Extraction 32

Convolutional Recurrent Network for Road Boundary Extraction

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

作者： Liang, Justin Homayounfar, Namdar Ma, Wei-Chiu Wang, Shenlong Urtasun, Raquel Uber Adv Technol Grp Pittsburgh PA 15201 USA Univ Toronto Toronto ON Canada MIT Cambridge MA 02139 USA

ISBN: (纸本)9781728132938

Creating high definition maps that contain precise information of static elements of the scene is of utmost importance for enabling self driving cars to drive safely. In this paper, we tackle the problem of drivable road boundary extraction from LiDAR and camera imagery. Towards this goal, we design a structured model where a fully convolutional network obtains deep features encoding the location and direction of road boundaries and then, a convolutional recurrent network outputs a polyline representation for each one of them. Importantly, our method is fully automatic and does not require a user in the loop. We showcase the effectiveness of our method on a large North American city where we obtain perfect topology of road boundaries 99.3% of the time at a high precision and recall.

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

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Monocular Depth Estimation Using Relative Depth Maps 32

Monocular Depth Estimation Using Relative Depth Maps

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

作者： Lee, Jae-Han Kim, Chang-Su Korea Univ Seoul South Korea

ISBN: (纸本)9781728132938

We propose a novel algorithm for monocular depth estimation using relative depth maps. First, using a convolutional neural network, we estimate relative depths between pairs of regions, as well as ordinary depths, at various scales. Second, we restore relative depth maps from selectively estimated data based on the rank-1 property of pairwise comparison matrices. Third, we decompose ordinary and relative depth maps into components and recombine them optimally to reconstruct a final depth map. Experimental results show that the proposed algorithm provides the state-of-art depth estimation performance.

关键词： 3D from Single Image Low-level vision

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Panoptic Segmentation 32

Panoptic Segmentation

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

作者： Kirillov, Alexander He, Kaiming Girshick, Ross Rother, Carsten Dollar, Piotr Facebook AI Res FAIR Austin TX 78719 USA Heidelberg Univ HCI IWR Heidelberg Germany

ISBN: (纸本)9781728132938

We propose and study a task we name panoptic segmentation (PS). Panoptic segmentation unifies the typically distinct tasks of semantic segmentation (assign a class label to each pixel) and instance segmentation (detect and segment each object instance). The proposed task requires generating a coherent scene segmentation that is rich and complete, an important step toward real-world vision systems. While early work in computer vision addressed related image/scene parsing tasks, these are not currently popular, possibly due to lack of appropriate metrics or associated recognition challenges. To address this, we propose a novel panoptic quality (PQ) metric that captures performance for all classes (stuff and things) in an interpretable and unified manner. Using the proposed metric, we perform a rigorous study of both human and machine performance for PS on three existing datasets, revealing interesting insights about the task. The aim of our work is to revive the interest of the community in a more unified view of image segmentation.

关键词： Categorization Grouping and Shape recognition: Detection Retrieval Segmentation

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On zero-shot recognition of generic objects 32

On zero-shot recognition of generic objects

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

作者： Hascoet, Tristan Ariki, Yasuo Takiguchi, Tetsuya Kobe Univ Grad Sch Syst Informat Kobe Hyogo Japan

ISBN: (纸本)9781728132938

Many recent advances in computer vision are the result of a healthy competition among researchers on high quality, task-specific, benchmarks. After a decade of active research, zero-shot learning (ZSL) models accuracy on the Imagenet benchmark remains far too low to be considered for practical object recognition applications. In this paper, we argue that the main reason behind this apparent lack of progress is the poor quality of this benchmark. We highlight major structural flaws of the current benchmark and analyze different factors impacting the accuracy of ZSL models. We show that the actual classification accuracy of existing ZSL models is significantly higher than was previously thought as we account for these flaws. We then introduce the notion of structural bias specific to ZSL datasets. We discuss how the presence of this new form of bias allows for a trivial solution to the standard benchmark and conclude on the need for a new benchmark. We then detail the semi-automated construction of a new benchmark to address these flaws.

关键词： Datasets and Evaluation vision + Language

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Aggregation Cross-Entropy for Sequence recognition 32

Aggregation Cross-Entropy for Sequence Recognition

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

作者： Xie, Zecheng Huang, Yaoxiong Zhu, Yuanzhi Jin, Lianwen Liu, Yuliang Xie, Lele South China Univ Technol Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781728132938

In this paper, we propose a novel method, aggregation cross-entropy (ACE), for sequence recognition from a brand new perspective. The ACE loss function exhibits competitive performance to CTC and the attention mechanism, with much quicker implementation (as it involves only four fundamental formulas), faster inference\back-propagation (approximately O(1) in parallel), less storage requirement (no parameter and negligible runtime memory), and convenient employment (by replacing CTC with ACE). Furthermore, the proposed ACE loss function exhibits two noteworthy properties: (1) it can be directly applied for 2D prediction by flattening the 2D prediction into 1D prediction as the input and (2) it requires only characters and their numbers in the sequence annotation for supervision, which allows it to advance beyond sequence recognition, e.g., counting problem.

关键词： Categorization Deep Learning Document Analysis recognition: Detection Retrieval Statistical Learning vision Applications and Systems

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Dichromatic Model Based Temporal Color Constancy for AC Light Sources 32

Dichromatic Model Based Temporal Color Constancy for AC Ligh...

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

作者： Yoo, Jun-Sang Kim, Jong-Ok Korea Univ Sch Elect Engn Seoul South Korea

ISBN: (纸本)9781728132938

Existing dichromatic color constancy approach commonly requires a number of spatial pixels which have high specularity. In this paper, we propose a novel approach to estimate the illuminant chromaticity of AC light source using high-speed camera. We found that the temporal observations of an image pixel at a fixed location distribute on an identical dichromatic plane. Instead of spatial pixels with high specularity, multiple temporal samples of a pixel are exploited to determine AC pixels for dichromatic plane estimation, whose pixel intensity is sinusoidally varying well. A dichromatic plane is calculated per each AC pixel, and illuminant chromaticity is determined by the intersection of dichromatic planes. From multiple dichromatic planes, an optimal illuminant is estimated with a novel MAP framework. It is shown that the proposed method outperforms both existing dichromatic based methods and temporal color constancy methods, irrespective of the amount of specularity.

关键词： computer vision Theory Low-level vision

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QATM: Quality-Aware Template Matching For Deep Learning 32

QATM: Quality-Aware Template Matching For Deep Learning

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

作者： Cheng, Jiaxin Wu, Yue Abd-Almageed, Wael Natarajan, Premkumar USC Informat Sci Inst Marina Del Rey CA 90292 USA

ISBN: (纸本)9781728132938

Finding a template in a search image is one of the core problems many computer vision, such as semantic image semantic, image-to-GPS verification etc. We propose a novel quality-aware template matching method, QATM, which is not only used as a standalone template matching algorithm, but also a trainable layer that can be easily embedded into any deep neural network. Here, our quality can be interpreted as the distinctiveness of matching pairs. Specifically, we assess the quality of a matching pair using soft-ranking among all matching pairs, and thus different matching scenarios such as 1-to-1, I-to-many, and many-to-many will be all reflected to different values. Our extensive evaluation on classic template matching benchmarks and deep learning tasks demonstrate the effectiveness of QATM. It not only outperforms state-of-the-art template matching methods when used alone, but also largely improves existing deep network solutions.

关键词： Categorization recognition: Detection Retrieval

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Multimodal Explanations by Predicting Counterfactuality in Videos 32

Multimodal Explanations by Predicting Counterfactuality in V...

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

作者： Kanehira, Atsushi Takemoto, Kentaro Inayoshi, Sho Harada, Tatsuya Preferred Networks Tokyo Japan Univ Tokyo Tokyo Japan RIKEN Wako Saitama Japan

ISBN: (纸本)9781728132938

This study addresses generating counterfactual explanations with multimodal information. Our goal is not only to classify a video into a specific category, but also to provide explanations on why it is not categorized to a specific class with combinations of visual-linguistic information. Requirements that the expected output should satisfy are referred to as counterfactuality in this paper: (1) Compatibility of visual-linguistic explanations, and (2) Positiveness/negativeness for the specific positive/negative class. Exploiting a spatio-temporal region (tube) and an attribute as visual and linguistic explanations respectively, the explanation model is trained to predict the counterfactuality for possible combinations of multimodal information in a post-hoc manner. The optimization problem, which appears during training/inference, can be efficiently solved by inserting a novel neural network layer, namely the maximum subpath layer. We demonstrated the effectiveness of this method by comparison with a baseline of the action recognition datasets extended for this task. Moreover, we provide information-theoretical insight into the proposed method.

关键词： Video Analytics vision + Language vision Applications and Systems

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Local Features and Visual Words Emerge in Activations 32

Local Features and Visual Words Emerge in Activations

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

作者： Simeoni, Oriane Avrithis, Yannis Chum, Ondrej Univ Rennes INRIA CNRS IRISA Rennes France Czech Tech Univ VRG FEE Prague Czech Republic

ISBN: (纸本)9781728132938

We propose a novel method of deep spatial matching (DSM) for image retrieval. Initial ranking is based on image descriptors extracted from convolutional neural network activations by global pooling, as in recent state-of-the-art work. However, the same sparse 3D activation tensor is also approximated by a collection of local features. These local features are then robustly matched to approximate the optimal alignment of the tensors. This happens without any network modification, additional layers or training. No local feature detection happens on the original image. No local feature descriptors and no visual vocabulary are needed throughout the whole process. We experimentally show that the proposed method achieves the state-of-the-art performance on standard benchmarks across different network architectures and different global pooling methods. The highest gain in performance is achieved when diffusion on the nearest-neighbor graph of global descriptors is initiated from spatially verified images.

关键词： Categorization computer vision Theory recognition: Detection Retrieval

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Neighbourhood Watch: Referring Expression Comprehension via Language-guided Graph Attention Networks 32

Neighbourhood Watch: Referring Expression Comprehension via ...

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

作者： Wang, Peng Wu, Qi Cao, Jiewei Shen, Chunhua Gao, Lianli van den Hengel, Anton Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia Univ Elect Sci & Technol China Chengdu Sichuan Peoples R China

ISBN: (纸本)9781728132938

The task in referring expression comprehension is to localize the object instance in an image described by a referring expression phrased in natural language. As a language-to-vision matching task, the key to this problem is to learn a discriminative object feature that can adapt to the expression used. To avoid ambiguity, the expression normally tends to describe not only the properties of the referent itself, but also its relationships to its neighbourhood. To capture and exploit this important information we propose a graph-based, language-guided attention mechanism. Being composed of node attention component and edge attention component, the proposed graph attention mechanism explicitly represents inter-object relationships, and properties with a flexibility and power impossible with competing approaches. Furthermore, the proposed graph attention mechanism enables the comprehension decision to be visualizable and explainable. Experiments on three referring expression comprehension datasets show the advantage of the proposed approach.

关键词： Categorization recognition: Detection Retrieval vision + Language

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