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检索条件"主题词=Recognition: Detection"

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P2SGrad: Refined Gradients for Optimizing Deep Face Models 32

P2SGrad: Refined Gradients for Optimizing Deep Face Models

引用

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

作者： Zhang, Xiao Zhao, Rui Yan, Junjie Gao, Mengya Qiao, Yu Wang, Xiaogang Li, Hongsheng Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol SIAT SenseTime Joint Lab Shenzhen Peoples R China

ISBN: (纸本)9781728132938

Cosine-based softmax losses [20, 29, 27, 3] significantly improve the performance of deep face recognition networks. However, these losses always include sensitive hyper-parameters which can make training process unstable, and it is very tricky to set suitable hyper parameters for a specific dataset. This paper addresses this challenge by directly designing the gradients for training in an adaptive manner. We first investigate and unify previous cosine softmax losses from the perspective of gradients. This unified view inspires us to propose a novel gradient called P2SGrad (Probability-to-Similarity Gradient), which leverages a cosine similarity instead of classification probability to control the gradients for updating neural network parameters. P2SGrad is adaptive and hyper-parameter free, which makes training process more efficient and faster. We evaluate our P2SGrad on three face recognition benchmarks, LFW [7], MegaFace [8], and IJB-C [16]. The results show that P2SGrad is stable in training, robust to noise, and achieves state-of-the-art performance on all the three benchmarks.

关键词： And Body Pose Biometrics Categorization Deep Learning Face Gesture Optimization Methods recognition: detection Retrieval

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MnasNet: Platform-Aware Neural Architecture Search for Mobile 32

MnasNet: Platform-Aware Neural Architecture Search for Mobil...

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

作者： Tan, Mingxing Chen, Bo Pang, Ruoming Vasudevan, Vijay Sandier, Mark Howard, Andrew Le, Quoc V. Google Brain Mountain View CA 94043 USA Google Inc Menlo Pk CA USA

ISBN: (纸本)9781728132938

Designing convolutional neural networks (CNN) for mobile devices is challenging because mobile models need to be small and fast, yet still accurate. Although significant efforts have been dedicated to design and improve mobile CNNs on all dimensions, it is very difficult to manually balance these trade-offs when there are so many architectural possibilities to consider. In this paper, we propose an automated mobile neural architecture search (MNAS) approach, which explicitly incorporate model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency. Unlike previous work, where latency is considered via another, often inaccurate proxy (e.g., FLOPS), our approach directly measures real-world inference latency by executing the model on mobile phones. To further strike the right balance between flexibility and search space size, we propose a novel factorized hierarchical search space that encourages layer diversity throughout the network. Experimental results show that our approach consistently outperforms state-of-the-art mobile CNN models across multiple vision tasks. On the ImageNet classification task, our MnasNet achieves 75.2% top-1 accuracy with 78ms latency on a Pixel phone, which is 1.8x faster than MobileNetV2 with 0.5% higher accuracy and 2.3x faster than NASNet with 1.2% higher accuracy. Our MnasNet also achieves better mAP quality than MobileNets for COCO object detection.

关键词： Categorization Deep Learning recognition: detection Retrieval

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Separate to Adapt: Open Set Domain Adaptation via Progressive Separation 32

Separate to Adapt: Open Set Domain Adaptation via Progressiv...

引用

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

作者： Liu, Hong Cao, Zhangjie Long, Mingsheng Wang, Jianmin Yang, Qiang Tsinghua Univ MOE KLiss BNRistSch Software Beijing Peoples R China Tsinghua Univ Res Ctr Big Data Beijing Peoples R China Beijing Key Lab Ind Big Data Syst & Applicat Beijing Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9781728132938

Domain adaptation has become a resounding success in leveraging labeled data from a source domain to learn an accurate classifier for an unlabeled target domain. When deployed in the wild, the target domain usually contains unknown classes that are not observed in the source domain. Such setting is termed Open Set Domain Adaptation (OSDA). While several methods have been proposed to address OSDA, none of them takes into account the openness of the target domain, which is measured by the proportion of unknown classes in all target classes. Openness is a critical point in open set domain adaptation and exerts a significant impact on performance. In addition, current work aligns the entire target domain with the source domain without excluding unknown samples, which may give rise to negative transfer due to the mismatch between unknown and known classes. To this end, this paper presents Separate to Adapt (STA), an end-to-end approach to open set domain adaptation. The approach adopts a coarse-to-fine weighting mechanism to progressively separate the samples of unknown and known classes, and simultaneously weigh their importance on feature distribution alignment. Our approach allows openness-robust open set domain adaptation, which can be adaptive to a variety of openness in the target domain. We evaluate STA on several benchmark datasets of various openness levels. Results verify that STA significantly outperforms previous methods.

关键词： Categorization Deep Learning recognition: detection Retrieval

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Translate-to-Recognize Networks for RGB-D Scene recognition 32

Translate-to-Recognize Networks for RGB-D Scene Recognition

引用

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

作者： Du, Dapeng Wang, Limin Wang, Huiling Zhao, Kai Wu, Gangshan Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9781728132938

Cross-modal transfer is helpful to enhance modality-specific discriminative power for scene recognition. To this end, this paper presents a unified framework to integrate the tasks of cross-modal translation and modality-specific recognition, termed as Translate-to-Recognize Network (TRecgNet). Specifically, both translation and recognition tasks share the same encoder network, which allows to explicitly regularize the training of recognition task with the help of translation, and thus improve its final generalization ability. For translation task, we place a decoder module on top of the encoder network and it is optimized with a new layer-wise semantic loss, while for recognition task, we use a linear classifier based on the feature embedding from encoder and its training is guided by the standard cross-entropy loss. In addition, our TRecgNet allows to exploit large numbers of unlabeled RGB-D data to train the translation task and thus improve the representation power of encoder network. Empirically, we verify that this new semi-supervised setting is able to further enhance the performance of recognition network. We perform experiments on two RGB-D scene recognition benchmarks: NYU Depth v2 and SUN RGB-D, demonstrating that TRecgNet achieves superior performance to the existing state-of-the-art methods, especially for recognition solely based on a single modality.

关键词： Categorization recognition: detection Representation Learning Retrieval RGBD sensors and analytics Scene Analysis and Under

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Bag of Tricks for Image Classification with Convolutional Neural Networks 32

Bag of Tricks for Image Classification with Convolutional Ne...

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

作者： He, Tong Zhang, Zhi Zhang, Hang Zhang, Zhongyue Xie, Junyuan Li, Mu Amazon Web Serv Seattle WA 98109 USA

ISBN: (纸本)9781728132938

Much of the recent progress made in image classification research can be credited to training procedure refinements, such as changes in data augmentations and optimization methods. In the literature, however, most refinements are either briefly mentioned as implementation details or only visible in source code. In this paper, we will examine a collection of such refinements and empirically evaluate their impact on the final model accuracy through ablation study. We will show that, by combining these refinements together, we are able to improve various CNN models significantly. For example, we raise ResNet-50's top-1 validation accuracy from 75.3% to 79.29% on ImageNet. We will also demonstrate that improvement on image classification accuracy leads to better transfer learning performance in other application domains such as object detection and semantic segmentation.

关键词： Categorization Deep Learning recognition: detection Retrieval

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Re-ranking via Metric Fusion for Object Retrieval and Person Re-identification 32

Re-ranking via Metric Fusion for Object Retrieval and Person...

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

作者： Bai, Song Tang, Peng Torr, Philip H. S. Latecki, Longin Jan Univ Oxford Oxford England Huazhong Univ Sci & Technol Wuhan Hubei Peoples R China Temple Univ Philadelphia PA 19122 USA

ISBN: (纸本)9781728132938

This work studies the unsupervised re-ranking procedure for object retrieval and person re-identification with a specific concentration on an ensemble of multiple metrics (or similarities). While the re-ranking step is involved by running a diffusion process on the underlying data manifolds, the fusion step can leverage the complementarity of multiple metrics. We give a comprehensive summary of existing fusion with diffusion strategies, and systematically analyze their pros and cons. Based on the analysis, we propose a unified yet robust algorithm which inherits their advantages and discards their disadvantages. Hence, we call it Unified Ensemble Diffusion (UED). More interestingly, we derive that the inherited properties indeed stem from a theoretical framework, where the relevant works can be elegantly summarized as special cases of UED by imposing additional constraints on the objective function and varying the solver of similarity propagation. Extensive experiments with 3D shape retrieval, image retrieval and person re-identification demonstrate that the proposed framework outperforms the state of the arts, and at the same time suggest that re-ranking via metric fusion is a promising tool to further improve the retrieval performance of existing algorithms.

关键词： 3D from Multiview and Sensors Categorization Grouping and Shape recognition: detection Representation Learning Retrieval Segmentation

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Learning a Deep ConvNet for Multi-label Classification with Partial Labels 32

Learning a Deep ConvNet for Multi-label Classification with ...

引用

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

作者： Durand, Thibaut Mehrasa, Nazanin Mori, Greg Borealis AI Toronto ON Canada Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781728132938

Deep ConvNets have shown great performance for single-label image classification (e.g. ImageNet), but it is necessary to move beyond the single-label classification task because pictures of everyday life are inherently multilabel. Multi-label classification is a more difficult task than single-label classification because both the input images and output label spaces are more complex. Furthermore, collecting clean multi-label annotations is more difficult to scale-up than single-label annotations. To reduce the annotation cost, we propose to train a model with partial labels i.e. only some labels are known per image. We first empirically compare different labeling strategies to show the potential for using partial labels on multi-label datasets. Then to learn with partial labels, we introduce a new classification loss that exploits the proportion of known labels per example. Our approach allows the use of the same training settings as when learning with all the annotations. We further explore several curriculum learning based strategies to predict missing labels. Experiments are performed on three large-scale multi-label datasets: MS COCO, NUS-WIDE and Open Images.

关键词： Categorization Deep Learning recognition: detection Retrieval

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Neural Sequential Phrase Grounding (SeqGROUND) 32

Neural Sequential Phrase Grounding (SeqGROUND)

引用

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

作者： Dogan, Pelin Sigal, Leonid Gross, Markus Swiss Fed Inst Technol Zurich Switzerland Univ British Columbia Vancouver BC Canada Vector Inst Toronto ON Canada Disney Res Zurich Switzerland

ISBN: (纸本)9781728132938

We propose an end-to-end approach for phrase grounding in images. Unlike prior methods that typically attempt to ground each phrase independently by building an imagetext embedding, our architecture formulates grounding of multiple phrases as a sequential and contextual process. Specifically, we encode region proposals and all phrases into two stacks of LSTM cells, along with so far grounded phrase-region pairs. These LSTM stacks collectively capture context for grounding of the next phrase. The resulting architecture, which we call SeqGROUND, supports many-to-many matching by allowing an image region to be matched to multiple phrases and vice versa. We show competitive performance on the Flickr30K benchmark dataset and, through ablation studies, validate the efficacy of sequential grounding as well as individual design choices in our model architecture.

关键词： Categorization recognition: detection Retrieval Vision + Language Visual Reasoning

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Data-Driven Neuron Allocation for Scale Aggregation Networks 32

Data-Driven Neuron Allocation for Scale Aggregation Networks

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

作者： Li, Yi Kuang, Zhanghui Chen, Yimin Zhang, Wayne SenseTime Hong Kong Peoples R China

ISBN: (纸本)9781728132938

Successful visual recognition networks benefit from aggregating information spanning from a wide range of scales. Previous research has investigated information fusion of connected layers or multiple branches in a block, seeking to strengthen the power of multi-scale representations. Despite their great successes, existing practices often allocate the neurons for each scale manually, and keep the same ratio in all aggregation blocks of an entire network, rendering suboptimal performance. In this paper, we propose to learn the neuron allocation for aggregating multiscale information in different building blocks of a deep network. The most informative output neurons in each block are preserved while others are discarded, and thus neurons for multiple scales are competitively and adaptively allocated. Our scale aggregation network (ScaleNet) is constructed by repeating a scale aggregation (SA) block that concatenates feature maps at a wide range of scales. Feature maps for each scale are generated by a stack of down sampling, convolution and upsampling operations. The data-driven neuron allocation and SA block achieve strong representational power at the cost of considerably low computational complexity. The proposed ScaleNet, by replacing all 3 x 3 convolutions in ResNet with our SA blocks, achieves better performance than ResNet and its outstanding variants like ResNeXt and SE-ResNet, in the same computational complexity. On ImageNet classification, ScaleNets absolutely reduce the top-I error rate of ResNets by 1.12 (101 layers) and 1.82 (50 layers). On COCO object detection, ScaleNets absolutely improve the mAP with backbone of ResNets by 3.6 and 4.6 on Faster-RCNN, respectively. Code and models are released on https://***/Eli-YiLi/ScaleNet.

关键词： Categorization Deep Learning recognition: detection Retrieval

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ToothNet: Automatic Tooth Instance Segmentation and Identification from Cone Beam CT Images 32

ToothNet: Automatic Tooth Instance Segmentation and Identifi...

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

作者： Cui, Zhiming Li, Changjian Wang, Wenping Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781728132938

This paper proposes a method that uses deep convolutional neural networks to achieve automatic and accurate tooth instance segmentation and identification from CBCT (cone beam CT) images for digital dentistry. The core of our method is a two-stage network. In the first stage, an edge map is extracted from the input CBCT image to enhance image contrast along shape boundaries. Then this edge map and the input images are passed to the second stage. In the second stage, we build our network upon the 3D region proposal network (RPN) with a novel learned-similarity matrix to help efficiently remove redundant proposals, speed up training and save GPU memory. To resolve the ambiguity in the identification task, we encode teeth spatial relationships as an additional feature input in the identification task, which helps to remarkably improve the identification accuracy. Our evaluation, comparison and comprehensive ablation studies demonstrate that our method produces accurate instance segmentation and identification results automatically and outperforms the state-of-the-art approaches. To the best of our knowledge, our method is the first to use neural networks to achieve automatic tooth segmentation and identification from CBCT images.

关键词： Biological and Cell Microscopy Categorization Grouping and Shape Medical recognition: detection Retrieval Segmentation

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