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检索条件"主题词=Recognition: Detection"

共 383 条记录，以下是21-30 订阅

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Pedestrian detection with Autoregressive Network Phases 32

Pedestrian Detection with Autoregressive Network Phases

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Brazil, Garrick Liu, Xiaoming Michigan State Univ E Lansing MI 48824 USA

ISBN: (纸本)9781728132938

We present an autoregressive pedestrian detection framework with cascaded phases designed to progressively improve precision. The proposed framework utilizes a novel lightweight stackable decoder-encoder module which uses convolutional re-sampling layers to improve features while maintaining efficient memory and runtime cost. Unlike previous cascaded detection systems, our proposed framework is designed within a region proposal network and thus retains greater context of nearby detections compared to independently processed RoI systems. We explicitly encourage increasing levels of precision by assigning strict labeling policies to each consecutive phase such that early phases develop features primarily focused on achieving high recall and later on accurate precision. In consequence, the final feature maps form more peaky radial gradients emulating from the centroids of unique pedestrians. Using our proposed autoregressive framework leads to new state-of-the-art performance on the reasonable and occlusion settings of the Caltech pedestrian dataset, and achieves competitive state-of-the-art performance on the KITTI dataset.

关键词： Categorization recognition: detection Retrieval Robotics + Driving Scene Analysis and Understanding Vision Applications and Systems

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Reasoning-RCNN: Unifying Adaptive Global Reasoning into Large-scale Object detection 32

Reasoning-RCNN: Unifying Adaptive Global Reasoning into Larg...

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Xu, Hang Jiang, ChenHan Liang, Xiaodan Lin, Liang Li, Zhenguo Huawei Noahs Ark Lab Beijing Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China

ISBN: (纸本)9781728132938

In this paper, we address the large-scale object detection problem with thousands of categories, which poses severe challenges due to long-tail data distributions, heavy occlusions, and class ambiguities. However, the dominant object detection paradigm is limited by treating each object region separately without considering crucial semantic dependencies among objects. In this work, we introduce a novel Reasoning-RCNN to endow any detection networks the capability of adaptive global reasoning over all object regions by exploiting diverse human commonsense knowledge. Instead of only propagating the visual features on the image directly, we evolve the high-level semantic representations of all categories globally to avoid distracted or poor visual features in the image. Specifically, built on feature representations of basic detection network, the proposed network first generates a global semantic pool by collecting the weights of previous classification layer for each category, and then adaptively enhances each object features via attending different semantic contexts in the global semantic pool. Rather than propagating information from all semantic information that may be noisy, our adaptive global reasoning automatically discovers most relative categories for feature evolving. Our Reasoning-RCNN is light-weight and flexible enough to enhance any detection backbone networks, and extensible for integrating any knowledge resources. Solid experiments on object detection benchmarks show the superiority of our Reasoning-RCNN, e.g. achieving around 16% improvement on VisualGenome, 37% on ADE in terms of mAP and 15% improvement on COCO.

关键词： Categorization Deep Learning recognition: detection Retrieval Visual Reasoning

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Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object detection for Autonomous Driving 32

Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap ...

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Wang, Yan Chao, Wei-Lun Garg, Divyansh Hariharan, Bharath Campbell, Mark Weinberger, Kilian Q. Cornell Univ Ithaca NY 14850 USA

ISBN: (纸本)9781728132938

3D object detection is an essential task in autonomous driving. Recent techniques excel with highly accurate detection rates, provided the 3D input data is obtained from precise but expensive LiDAR technology. Approaches based on cheaper monocular or stereo imagery data have, until now, resulted in drastically lower accuracies - a gap that is commonly attributed to poor image-based depth estimation. However, in this paper we argue that it is not the quality of the data but its representation that accounts for the majority of the difference. Taking the inner workings of convolutional neural networks into consideration, we propose to convert image-based depth maps to pseudo-LiDAR representations - essentially mimicking the LiDAR signal. With this representation we can apply different existing LiDAR-based detection algorithms. On the popular KITTI benchmark, our approach achieves impressive improvements over the existing state-of-the-art in image-based performance - raising the detection accuracy of objects within the 30m range from the previous state-of-the-art of 22% to an un-precedented 74%. At the time of submission our algorithm holds the highest entry on the KITTI 3D object detection leaderboard for stereo-image-based approaches.

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

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Strong-Weak Distribution Alignment for Adaptive Object detection 32

Strong-Weak Distribution Alignment for Adaptive Object Detec...

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Saito, Kuniaki Ushiku, Yoshitaka Harada, Tatsuya Saenko, Kate Boston Univ Boston MA 02215 USA Univ Tokyo Tokyo Japan RIKEN Tokyo Japan

ISBN: (纸本)9781728132938

We propose an approachfor unsupervised adaptation of object detectors from label-rich to label-poor domains which can significantly reduce annotation costs associated with detection. Recently, approaches that align distributions of source and target images using an adversarial loss have been proven effective for adapting object classifiers. However for object detection,fully matching the entire distributions of source and target images to each other at the global image level may fail, as domains could have distinct scene layouts and different combinations of objects. On the other hand, strong matching of local features such as texture and color makes sense, as it does not change category level semantics. This motivates us to propose a novel method for detectorad aptation based on strong local alignment and weak global alignment. Our key contribution is the weak alignment model, which focuses the adversarial alignment loss on images that are globally similar and puts less emphasis on aligning images that are globally dissimilar Additionally, we design the strong domain alignment model to only look at local receptive fields of the feature map. We empirically verify the effectiveness of our method on four datasets comprising both large and small domain shifts.

关键词： Categorization Deep Learning recognition: detection Representation Learning Retrieval

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Towards Accurate One-Stage Object detection with AP-Loss 32

Towards Accurate One-Stage Object Detection with AP-Loss

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Chen, Kean Li, Jianguo Lin, Weiyao See, John Wang, Ji Duan, Lingyu Chen, Zhibo He, Changwei Zou, Junni Shanghai Jiao Tong Univ Shanghai Peoples R China Intel Labs Beijing Peoples R China Multimedia Univ Cyberjaya Malaysia Tencent YouTu Lab Shanghai Peoples R China Peking Univ Beijing Peoples R China

ISBN: (纸本)9781728132938

One-stage object detectors are trained by optimizing classification-loss and localization-loss simultaneously, with the former suffering much from extreme foreground-background class imbalance issue due to the large number of anchors. This paper alleviates this issue by proposing a novel framework to replace the classification task in one-stage detectors with a ranking task, and adopting the Average-Precision loss (AP-loss) for the ranking problem. Due to its non-differentiability and non-convexity, the AP-loss cannot be optimized directly. For this purpose, we develop a novel optimization algorithm, which seamlessly combines the error-driven update scheme in perceptron learning and backpropagation algorithm in deep networks. We verify good convergence property of the proposed algorithm theoretically and empirically. Experimental results demonstrate notable performance improvement in state-of-the-art one-stage detectors based on AP-loss over different kinds of classification-losses on various benchmarks, without changing the network architectures.

关键词： Categorization recognition: detection Retrieval

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Moving Object detection under Discontinuous Change in Illumination Using Tensor Low-Rank and Invariant Sparse Decomposition 32

Moving Object Detection under Discontinuous Change in Illumi...

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Shakeri, Moein Zhang, Hong Univ Alberta Dept Comp Sci Edmonton AB Canada

ISBN: (纸本)9781728132938

Although low-rank and sparse decomposition based methods have been successfully applied to the problem of moving object detection using structured sparsity-inducing norms, they are still vulnerable to significant illumination changes that arise in certain applications. We are interested in moving object detection in applications involving time-lapse image sequences for which current methods mistakenly group moving objects and illumination changes into foreground. Our method relies on the multilinear (tensor) data low-rank and sparse decomposition framework to address the weaknesses of existing methods. The key to our proposed method is to create first a set of prior maps that can characterize the changes in the image sequence due to illumination. We show that they can be detected by a k-support norm. To deal with concurrent, two types of changes, we employ two regularization terms, one for detecting moving objects and the other for accounting for illumination changes, in the tensor low-rank and sparse decomposition formulation. Through comprehensive experiments using challenging datasets, we show that our method demonstrates a remarkable ability to detect moving objects under discontinuous change in illumination, and outperforms the state-of-the-art solutions to this challenging problem.

关键词： Categorization Grouping and Shape Image and Video Synthesis recognition: detection Representation Learning Retrieval Segmentation Video Analytic

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Multi-Task Multi-Sensor Fusion for 3D Object detection 32

Multi-Task Multi-Sensor Fusion for 3D Object Detection

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Liang, Ming Yang, Bin Chen, Yun Hu, Rui Urtasun, Raquel Uber Adv Technol Grp Pittsburgh PA 15201 USA Univ Toronto Toronto ON Canada Uber AI Residency Program San Francisco CA USA

ISBN: (纸本)9781728132938

In this paper we propose to exploit multiple related tasks for accurate multi-sensor 3D object detection. Towards this goal we present an end-to-end learnable architecture that reasons about 2D and 3D object detection as well as ground estimation and depth completion. Our experiments show that all these tasks are complementary and help the network learn better representations by fusing information at various levels. Importantly, our approach leads the KITTI benchmark on 2D, 3D and bird's eye view object detection, while being real-time.

关键词： 3D from Multiview and Sensors Categorization Deep Learning recognition: detection Representation Learning Retrieval Robotics + Driving

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Exploring Object Relation in Mean Teacher for Cross-Domain detection 32

Exploring Object Relation in Mean Teacher for Cross-Domain D...

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Cai, Qi Pan, Yingwei Ngo, Chong-Wah Tian, Xinmei Duan, Lingyu Yao, Ting Univ Sci & Technol China Hefei Peoples R China JD AI Res Beijing Peoples R China City Univ Hong Kong Kowloon Hong Kong Peoples R China Peking Univ Beijing Peoples R China

ISBN: (纸本)9781728132938

Rendering synthetic data (e.g., 3D CAD-rendered images) to generate annotations for learning deep models in vision tasks has attracted increasing attention in recent years. However, simply applying the models learnt on synthetic images may lead to high generalization error on real images due to domain shift. To address this issue, recent progress in cross-domain recognition has featured the Mean Teacher, which directly simulates unsupervised domain adaptation as semi-supervised learning. The domain gap is thus naturally bridged with consistency regularization in a teacher-student scheme. In this work, we advance this Mean Teacher paradigm to be applicable for cross domain detection. Specifically, we present Mean Teacher with Object Relations (MTOR) that novelly remolds Mean Teacher under the backbone of Faster R-CNN by integrating the object relations into the measure of consistency cost between teacher and student modules. Technically, MTOR firstly learns relational graphs that capture similarities between pairs of regions for teacher and student respectively. The whole architecture is then optimized with three consistency regularizations: 1) region-level consistency to align the region-level predictions between teacher and student, 2) inter-graph consistency for matching the graph structures between teacher and student, and 3) intra-graph consistency to enhance the similarity between regions of same class within the graph of student. Extensive experiments are conducted on the transfers across Cityscapes, Foggy Cityscapes, and SIMlOk, and superior results are reported when comparing to state-of-the-art approaches. More remarkably, we obtain a new record of single model: 22.8% of mAP on Syn2Real detection dataset.

关键词： Categorization recognition: detection Retrieval

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High-level Semantic Feature detection: A New Perspective for Pedestrian detection 32

High-level Semantic Feature Detection: A New Perspective for...

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Liu, Wei Liao, Shengcai Ren, Weiqiang Hu, Weidong Yu, Yinan Natl Univ Def Technol Coll Elect Sci ATR Changsha Peoples R China Chinese Acad Sci Inst Automat CBSR Beijing Peoples R China Chinese Acad Sci Inst Automat NLPR Beijing Peoples R China Incept Inst Artificial Intelligence IIAI Abu Dhabi U Arab Emirates Horizon Robot Inc Beijing Peoples R China CASIA Beijing Peoples R China

ISBN: (纸本)9781728132938

Object detection generally requires sliding-window classifiers in tradition or anchor-based predictions in modern deep learning approaches. However, either of these approaches requires tedious configurations in windows or anchors. In this paper,taking pedestrian detection as an example, we provide a new perspective where detecting objects is motivated as a high-level semantic feature detection task. Like edges, corners, blobs and other feature detectors, the proposed detector scans for feature points all over the image, for which the convolution is naturally suited. However unlike these traditional low-level features, the proposed detector goes for a higher-level abstraction, that is, we are looking for central points where there are pedestrians,and modern deep models arealready capable of such a high-level semantic abstraction. Besides, like blob detection, we also predict the scales of the pedestrian points, which is also a straightforward convolution. Therefore, in this paper pedestrian detection is simplified as a straightforward center and scale prediction task through convolutions. This way, the proposed method enjoys an anchor-free setting. Though structurally simple, it presents competitive accuracy and good speed on challenging pedestrian detection benchmarks, and hence leading to a new attractive pedestrian detector.

关键词： Categorization Deep Learning recognition: detection Retrieval

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D2-Net: A Trainable CNN for Joint Description and detection of Local Features 32

D2-Net: A Trainable CNN for Joint Description and Detection ...

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32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Dusmanu, Mihai Rocco, Ignacio Pajdla, Tomas Pollefeys, Marc Sivic, Josef Torii, Akihiko Sattler, Torsten PSL Res Univ CNRS ENS DI F-75005 Paris France Inria Rocquencourt France Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland CTU CIIRC Prague Czech Republic Microsoft Redmond WA USA Tokyo Inst Technol Tokyo Japan Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9781728132938

In this work we address the problem of finding reliable pixel-level correspondences under difficult imaging conditions. We propose an approach where a single convolutional neural network plays a dual role: It is simultaneously a dense feature descriptor and a feature detector By postponing the detection to a later stage, the obtained keypoints are more stable than their traditionalcounterparts based on early detection of low-level structures. We show that this model can be trainedusing pixel correspondences extractedfrom readilyavailable large-scaleSfM reconstructions, without any further annotations. The proposed method obtains state-of-the-artperformance on both the difficult Aachen Day-Night localizationdataset and the InLoc indoor localization benchmark, as well as competitive performance on other benchmarksfor image matching and 3D reconstruction.

关键词： 3D from Multiview and Sensors Categorization Deep Learning Low-level Vision recognition: detection Retrieval

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