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检索条件"主题词=Recognition: Detection"

共 383 条记录，以下是11-20 订阅

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Pyramid Feature Attention Network for Saliency detection 32

Pyramid Feature Attention Network for Saliency detection

引用

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

作者： Zhao, Ting Wu, Xiangqian Harbin Inst Technol Sch Comp Sci & Technol Harbin Heilongjiang Peoples R China

ISBN: (纸本)9781728132938

Saliency detection is one of the basic challenges in computer vision. Recently, CNNs are the most widely used and powerful techniques for saliency detection, in which feature maps from different layers are always integrated without distinction. However, instinctively, the different feature maps of CNNs and the different features in the same maps should play different roles in saliency detection. To address this problem, a novel CNN named pyramid feature attention network (PFAN) is proposed to enhance the high-level context features and the low-level spatial structural features. In the proposed PFAN, a context-aware pyramid feature extraction (CPFE) module is designed for multi-scale high-level feature maps to capture the rich context features. A channel-wise attention (CA) model and a spatial attention (SA) model are respectively applied to the CPFE feature maps and the low-level feature maps, and then fused to detect salient regions. Finally, an edge preservation loss is proposed to get the accurate boundaries of salient regions. The proposed PFAN is extensively evaluated on five benchmark datasets and the experimental results demonstrate that the proposed network outperforms the state-of-the-art approaches under different evaluation metrics.

关键词： Categorization Deep Learning Grouping and Shape recognition: detection Retrieval Segmentation

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Triply Supervised Decoder Networks for Joint detection and Segmentation 32

Triply Supervised Decoder Networks for Joint Detection and S...

引用

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

作者： Cao, Jiale Pang, Yanwei Li, Xuelong Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China Northwestern Polytech Univ Ctr Opt IMagery Anal & Learning Xian Peoples R China

ISBN: (纸本)9781728132938

Joint object detection and semantic segmentation is essential in many fields such as self-driving cars. An initial attempt towards this goal is to simply share a single network for multi-task learning. We argue that it does not make full use of the fact that detection and segmentation are mutually beneficial. In this paper, we propose a framework called TripleNet to deeply boost these two tasks. On the one hand, to deeply join the two tasks at different scales, triple supervisions including detection-oriented supervision and class-aware/agnostic segmentation supervisions are imposed on each layer of the decoder. Class-agnostic segmentation provides an objectness prior to detection and segmentation. On the other hand, to further intercross the two tasks and refine the features in each scale, two light-weight modules (i.e., the inner-connected module and the attention skip-layer fusion) are incorporated. Because segmentation supervision on each decoder layer are not performed at the test stage and two added modules are light-weight, the proposed TripleNet can run at a real-time speed (16 fps). Experiments on the VOC 2007/2012 and COCO datasets show that TripleNet outperforms all the other one-stage methods on both two tasks (e.g., 81.9% mAP and 83.3% mIoU on VOC 2012, and 37.1% mAP and 59.6% mIoU on COCO) by a single network.

关键词： Categorization recognition: detection Retrieval Scene Analysis and Understanding

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Learning RoI Transformer for Oriented Object detection in Aerial Images 32

Learning RoI Transformer for Oriented Object Detection in Ae...

引用

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

作者： Ding, Jian Xue, Nan Long, Yang Xia, Gui-Song Lu, Qikai Wuhan Univ LIESMARS CAPTAIN Wuhan 430079 Peoples R China

ISBN: (纸本)9781728132938

Object detection in aerial images is an active yet challenging task in computer vision because of the bird's-eye view perspective, the highly complex backgrounds, and the variant appearances of objects. Especially when detecting densely packed objects in aerial images, methods relying on horizontal proposals for common object detection often introduce mismatches between the Region of Interests (Rocs) and objects. This leads to the common misalignment between the final object classification confidence and localization accuracy. In this paper, we propose a RoI Transformer to address these problems. The core idea of RoI Transformer is to apply spatial transformations on RoIs and learn the transformation parameters under the supervision of oriented bounding box (OBB) annotations. RoI Transformer is with lightweight and can be easily embedded into detectors for oriented object detection. Simply apply the RoI Transformer to light-head RCNN has achieved state-of-the-art performances on two common and challenging aerial datasets, i.e., DOTA and HRSC2016, with a neglectable reduction to detection speed. Our RoI Transformer exceeds the deformable Position Sensitive RoI pooling when oriented bounding-box annotations are available. Extensive experiments have also validated the flexibility and effectiveness of our RoI Transformer.

关键词： Categorization Deep Learning recognition: detection Retrieval Vision Applications and Systems

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Libra R-CNN: Towards Balanced Learning for Object detection 32

Libra R-CNN: Towards Balanced Learning for Object Detection

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

作者： Pang, Jiangmiao Chen, Kai Shi, Jianping Feng, Huajun Ouyang, Wanli Lin, Dahua Zhejiang Univ Hangzhou Zhejiang Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (纸本)9781728132938

Compared with model architectures, the training process, which is also crucial to the success of detectors, has received relatively less attention in object detection. In this work, we carefully revisit the standard training practice of detectors, and find that the detection performance is often limited by the imbalance during the training process, which generally consists in three levels - sample level, feature level, and objective level. To mitigate the adverse effects caused thereby, we propose Libra R-CNN, a simple but effective framework towards balanced learning for object detection. It integrates three novel components: IoU-balanced sampling, balanced feature pyramid, and balanced L1 loss, respectively for reducing the imbalance at sample, feature, and objective level. Benefitted from the overall balanced design, Libra R-CNN significantly improves the detection performance. Without bells and whistles, it achieves 2.5 points and 2.0 points higher Average Precision (AP) than FPN Faster R-CNN and RetinaNet respectively on MSCOCO.

关键词： Categorization Deep Learning recognition: detection Retrieval

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GS3D: An Efficient 3D Object detection Framework for Autonomous Driving 32

GS3D: An Efficient 3D Object Detection Framework for Autonom...

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

作者： Li, Buyu Ouyang, Wanli Sheng, Lu Zeng, Xingyu Wang, Xiaogang Chinese Univ Hong Kong CHUK SenseTime Joint Lab Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China Univ Sydney Sydney NSW Australia Beihang Univ Beijing Peoples R China

ISBN: (纸本)9781728132938

We present an efficient 3D object detection framework based on a single RGB image in the scenario of autonomous driving. Our efforts are put on extracting the underlying 3D information in a 2D image and determining the accurate 3D bounding box of the object without point cloud or stereo data. Leveraging the off-the-shelf 2D object detector, we propose an artful approach to efficiently obtain a coarse cuboid for each predicted 2D box. The coarse cuboid has enough accuracy to guide us to determine the 3D box of the object by refinement. In contrast to previous state-of-the-art methods that only use the features extracted from the 2D bounding box for box refinement, we explore the 3D structure information of the object by employing the visual features of visible surfaces. The new features from surfaces are utilized to eliminate the problem of representation ambiguity brought by only using a 2D bounding box. Moreover, we investigate different methods of 3D box refinement and discover that a classification formulation with quality aware loss has much better performance than regression. Evaluated on the KITTI benchmark, our approach outperforms current state-of-the-art methods for single RGB image based 3D object detection.

关键词： 3D from Single Image Categorization recognition: detection Retrieval

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Spatial-aware Graph Relation Network for Large-scale Object detection 32

Spatial-aware Graph Relation Network for Large-scale Object ...

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

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

ISBN: (纸本)9781728132938

How to proper encode high-order object relation in the detection system without any external knowledge? How to leverage the information between co-occurrence and locations of objects for better reasoning? These questions are key challenges towards large-scale object detection system that aims to recognize thousands of objects entangled with complex spatial and semantic relationships nowadays. Distilling key relations that may affect object recognition is crucially important since treating each region separately leads to a big performance drop when facing heavy long-tail data distributions and plenty of confusing categories. Recent works try to encode relation by constructing graphs, e.g. using handcraft linguistic knowledge between classes or implicitly learning a fully-connected graph between regions. However, the handcraft linguistic knowledge cannot be individualizedfor each image due to the semantic gap between linguistic and visual context while the fully-connected graph is inefficient and noisy by incorporating redundant and distracted relations/edges from irrelevant objects and backgrounds. In this work, we introduce a Spatial-aware Graph Relation Network (SGRN) to adaptive discover and incorporate key semantic and spatial relationships for reasoning over each object. Our method considers the relative location layouts and interactions among which can be easily injected into any detection pipelines to boost the performance. Specifically, our SGRN integrates a graph learner module for learning a interpatable sparse graph structure to encode relevant contextual regions and a spatial graph reasoning module with learnable spatial Gaussian kernels to perform graph inference with spatial awareness. Extensive experiments verify the effectiveness of our method, e.g. achieving around 32% improvement on VG(3000 classes) and 28% on ADE in terms of mAP.

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

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Less is More: Learning Highlight detection from Video Duration 32

Less is More: Learning Highlight Detection from Video Durati...

引用

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

作者： Xiong, Bo Kalantidis, Yannis Ghadiyaram, Deepti Grauman, Kristen Univ Texas Austin Austin TX 78712 USA Facebook AI Menlo Pk CA USA Facebook AI Res Menlo Pk CA 94025 USA

ISBN: (纸本)9781728132938

Highlight detection has the potential to significantly ease video browsing, but existing methods often suffer from expensive supervision requirements, where human viewers must manually identify highlights in training videos. We propose a scalable unsupervised solution that exploits video duration as an implicit supervision signal. Our key insight is that video segments from shorter user-generated videos are more likely to be highlights than those from longer videos, since users tend to be more selective about the content when capturing shorter videos. Leveraging this insight, we introduce a novel ranking framework that prefers segments from shorter videos, while properly accounting for the inherent noise in the (unlabeled) training data. We use it to train a highlight detector with 10M hashtagged Instagram videos. In experiments on two challenging public video highlight detection benchmarks, our method substantially improves the state-of-the-art for unsupervised highlight detection.

关键词： Action recognition Categorization recognition: detection Retrieval Video Analytics

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Multi-task Self-supervised Object detection via Recycling of Bounding Box Annotations 32

Multi-task Self-supervised Object Detection via Recycling of...

引用

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

作者： Lee, Wonhee Na, Joonil Kim, Gunhee Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9781728132938

In spite of recent enormous success of deep convolutional networks in object detection, they require a large amount of bounding box annotations, which are often time-consuming and error-prone to obtain. To make better use of given limited labels, we propose a novel object detection approach that takes advantage of both multi-task learning (MTL) and self-supervised learning (SSL). We propose a set of auxiliary tasks that help improve the accuracy of object detection. They create their own labels by recycling the bounding box labels (i.e. annotations of the main task) in an SSL manner, and are jointly trained with the object detection model in an MTL way. Our approach is integrable with any region proposal based detection models. We empirically validate that our approach effectively improves detection performance on various architectures and datasets. We test two state-of-the-art region proposal object detectors, including Faster R-CNN [39] and R-FCN [10], with three CNN backbones of ResNet-101 [22], Inception-ResNet-v2 [45], and MobileNet on two benchmark datasets of PASCAL VOC [14] and COCO[30].

关键词： Categorization recognition: detection Retrieval

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Bounding Box Regression with Uncertainty for Accurate Object detection 32

Bounding Box Regression with Uncertainty for Accurate Object...

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

作者： He, Yihui Zhu, Chenchen Wang, Jianren Savvides, Marios Zhang, Xiangyu Carnegie Mellon Univ Pittsburgh PA 15213 USA Megvii Inc Face Beijing Peoples R China

ISBN: (纸本)9781728132938

Large-scale object detection datasets (e.g., MS-COCO) try to define the ground truth bounding boxes as clear as possible. However, we observe that ambiguities are still introduced when labeling the bounding boxes. In this paper, we propose a novel bounding box regression loss for learning bounding box transformation and localization variance together. Our loss greatly improves the localization accuracies of various architectures with nearly no additional computation. The learned localization variance allows us to merge neighboring bounding boxes during non-maximum suppression (NMS), which further improves the localization performance. On MS-COCO, we boost the Average Precision (AP) of VGG-16 Faster R-CNN from 23.6% to 29.1%. More importantly, for ResNet-50-FPN Mask R-CNN, our method improves the AP and AP(90) by 1.8% and 6.2% respectively, which significantly outperforms previous stateof-the-art bounding box refinement methods. Our code and models are available at ***/yihui-he/KL-Loss

关键词： Categorization recognition: detection Retrieval

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Learning to Learn Relation for Important People detection in Still Images 32

Learning to Learn Relation for Important People Detection in...

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

作者： Li, Wei-Hong Hong, Fa-Ting Zheng, Wei-Shi Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China Univ Edinburgh Sch Informat VICO Grp Edinburgh Midlothian Scotland Accuvis Technol Co Ltd Abu Dhabi U Arab Emirates Minist Educ Key Lab Machine Intelligence & Adv Comp Beijing Peoples R China

ISBN: (纸本)9781728132938

Humans can easily recognize the importance of people in social event images, and they always focus on the most important individuals. However, learning to learn the relation between people in an image, and inferring the most important person based on this relation, remains undeveloped. In this work, we propose a deep imPOrtance relatIon NeTwork (POINT) that combines both relation modeling and feature learning. In particular, we infer two types of interaction modules: the person-person interaction module that learns the interaction between people and the event-person interaction module that learns to describe how a person is involved in the event occurring in an image. We then estimate the importance relations among people from both interactions and encode the relation feature from the importance relations. In this way, POINT automatically learns several types of relation features in parallel, and we aggregate these relation features and the person's feature to form the importance feature for important people classification. Extensive experimental results show that our method is effective for important people detection and verify the efficacy of learning to learn relations for important people detection.

关键词： And Body Pose Categorization Face Gesture recognition: detection Representation Learning Retrieval

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