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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

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Analysis of efficient CNN design techniques for semantic segmentation 31

Analysis of efficient CNN design techniques for semantic seg...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Briot, Alexandre Viswanath, Prashanth Yogamani, Senthil Valeo Creteil GEEDS AI Paris France Valeo Vis Syst Galway Ireland

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Majority of CNN architecture design is aimed at achieving high accuracy in public benchmarks by increasing the complexity. Typically, they are over-specified by a large margin and can be optimized by a factor of 10-100x with only a small reduction in accuracy. In spite of the increase in computational power of embedded systems, these networks are still not suitable for embedded deployment. There is a large need to optimize for hardware and reduce the size of the network by orders of magnitude for computer vision applications. This has led to a growing community which is focused on designing efficient networks. However, CNN architectures are evolving rapidly and efficient architectures seem to lag behind. There is also a gap in understanding the hardware architecture details and incorporating it into the network design. The motivation of this paper is to systematically summarize efficient design techniques and provide guidelines for an application developer. We also perform a case study by benchmarking various semantic segmentation algorithms for autonomous driving.

关键词： Convolution computer architecture Semantics Hardware Quantization (signal) Kernel Computational modeling

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Reconstructing Spectral Images from RGB-Images using a Convolutional Neural Network 31

Reconstructing Spectral Images from RGB-Images using a Convo...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Stiebel, Tarek Koppers, Simon Seltsam, Philipp Merhof, Dorit Rhein Westfal TH Aachen Inst Imaging & Comp Vis Aachen Germany

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Recovering high-dimensional spectral images taken with spectrally low-dimensional camera systems, in the extreme case RGB-images, has been of great interest for a variety of applications. An accurate spectral reconstruction is typically required to either achieve a better color accuracy or to improve object recognition/classification tasks. Almost all published work to date aims at performing a mapping from individual camera signals towards the corresponding spectrum. However, it might be beneficial to consider not only single pixels, but also contextual information. Here, we propose a convolutional neural network architecture that learns a mapping from RGB- to spectral images. We trained the network on the largest hyper-spectral data set available to date [;'I and analyzed the influence of different error metrics as loss functions. An objective evaluation of the performance in comparison to state of the art spectral reconstruction techniques is given by participating in the NTIRE 2018 challenge on spectral reconstruction [1].

关键词： computer vision pattern recognition conferences

来源：评论

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Human Perceptions of Sensitive Content in Photos 31

Human Perceptions of Sensitive Content in Photos

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Li, Yifang Troutman, Wyatt Knijnenburg, Bart P. Caine, Kelly Clemson Univ Clemson SC 29631 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Before we can obfuscate portions of an image to enhance privacy, we must know what portions are considered sensitive. In this paper, we report results from a study aimed at identifying sensitive content in photos from a human-centered perspective. We collected sensitive photos and/or descriptions of sensitive photos from participants and asked them to identify which elements of the photo made each photo sensitive. Using this information, we propose an initial two-level taxonomy of sensitive content categories. This taxonomy may be useful to privacy researchers, online social network designers, policy makers, computer vision researchers and anyone wishing to identify potentially sensitive content in photos. We conclude by providing insights about how these results may be used to enhance computer vision approaches to protecting image privacy.

关键词： Privacy computer vision Security Law Interviews Taxonomy

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Uncertainty Gated Network for Land Cover Segmentation 31

Uncertainty Gated Network for Land Cover Segmentation

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Pascual, Guillem Segui, Santi Vioia, Joidi Univ Barcelona Barcelona Spain

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

The production of thematic maps depicting land cover is one of the most common applications of remote sensing. To this end, several semantic segmentation approaches, based on deep learning, have been proposed in the literature, but land cover segmentation is still considered an open problem due to some specific problems related to remote sensing imaging. In this paper we propose a novel approach to deal with the problem of modelling multiscale contexts surrounding pixels of different land cover categories. The approach leverages the computation of a heteroscedastic measure of uncertainty when classifying individual pixels in an image. This classification uncertainty measure is used to define a set of memory gates between layers that allow a principled method to select the optimal decision for each pixel.

关键词： Uncertainty Image segmentation Logic gates Semantics computer vision Measurement uncertainty Image resolution

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Jersey Number recognition with Semi-Supervised Spatial Transformer Network 31

Jersey Number Recognition with Semi-Supervised Spatial Trans...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Li, Gen Xu, Shikun Liu, Xiang Li, Lei Wang, Changhu Toutiao AI Lab Beijing Peoples R China Beijing Univ Posts & Telecoms Beijing Peoples R China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

It is still a challenging task to recognize the jersey number of players on the court in soccer match videos, as the jersey numbers are very small in the object detection task and annotated data are not easy to collect. Based on the object detection results of all the players on the court, a CNN model is first introduced to classify these numbers on the deteced players' images. To localize the jersey number more precisely without involving another digit detector and extra consumption, we then improve the former network to an end-to-end framework by fusing with the spatial transformer network (STN). To further improve the accuracy, we bring extra supervision to STN and upgrade the model to a semi-supervised multi-task learning system, by labeling a small portion of the number areas in the dataset by quadrangle. Extensive experiments illustrate the effectiveness of the proposed framework.

关键词： Feature extraction Sports Videos Neural networks Image recognition Data models Task analysis

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Discovering Style Trends through Deep Visually Aware Latent Item Embeddings 31

Discovering Style Trends through Deep Visually Aware Latent ...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Iqbal, Murium Kovac, Adair Aryafar, Kamelia Overstock Com Midvale UT 84047 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In this paper, we explore Latent Dirichlet Allocation (LDA) [1] and Polylingual Latent Dirichlet Allocation (PolyLDA) [6], as a means to discover trending styles in Overstock 1 from deep visual semantic features transferred from a pretrained convolutional neural network and text-based item attributes. To utilize deep visual semantic features in conjunction with LDA, we develop a method for creating a bag of words representation of unrolled image vectors. By viewing the channels within the convolutional layers of a Resnet-50 [2] as being representative of a word, we can index these activations to create visual documents. We then train LDA over these documents to discover the latent style in the images. We also incorporate text-based data with PolyLDA, where each representation is viewed as an independent language attempting to describe the same style. The resulting topics are shown to be excellent indicators of visual style across our platform.

关键词： Visualization Semantics Image color analysis Shape Vocabulary Data models computer vision

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Pay Attention to Virality: understanding popularity of social media videos with the attention mechanism 31

Pay Attention to Virality: understanding popularity of socia...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Bielski, Adam Trzcinski, Tomasz Tooploox Wroclaw Poland Warsaw Univ Technol Tooploox Warsaw Poland

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Predicting popularity of social media videos before they are published is a challenging task, mainly due to the complexity of content distribution network as well as the number of factors that play part in this process. As solving this task provides tremendous help for media content creators, many successful methods were proposed to solve this problem with machine learning. In this work, we change the viewpoint and postulate that it is not only the predicted popularity that matters, but also, maybe even more importantly, understanding of how individual parts influence the final popularity score. To that end, we propose to combine the Grad-CAM visualization method with a soft attention mechanism. Our preliminary results show that this approach allows for more intuitive interpretation of the content impact on video popularity, while achieving competitive results in terms of prediction accuracy.

关键词： Videos Visualization Social network services conferences Task analysis Heating systems computer vision

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Large Receptive Field Networks for High-Scale Image Super-Resolution 31

Large Receptive Field Networks for High-Scale Image Super-Re...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Seif, George Androutsos, Dimitrios Ryerson Univ Toronto ON Canada

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Convolutional Neural Networks have been the backbone of recent rapid progress in Single-Image Super-Resolution. However, existing networks are very deep with many network parameters, thus having a large memory footprint and being challenging to train. We propose Large Receptive Field Networks which strive to directly expand the receptive field of Super-Resolution networks without increasing depth or parameter count. In particular, we use two different methods to expand the network receptive field: 1-D separable kernels and atrous convolutions. We conduct considerable experiments to study the performance of various arrangement schemes of the 1-D separable kernels and atrous convolution in terms of accuracy (PSNR / SSIM), parameter count, and speed, while focusing on the more challenging high upscaling factors. Extensive benchmark evaluations demonstrate the effectiveness of our approach.

关键词： Kernel Image resolution Convolutional codes Convolution Signal resolution Memory management computer vision

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Deep Disguised Faces recognition 31

Deep Disguised Faces Recognition

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Kaipeng Chang, Ya-Liang Hsu, Winston Natl Taiwan Univ Taipei Taiwan

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Recently, deep learning based approaches have yielded a significant improvement in face recognition in the wild. However, "disguised face" recognition is still a challenging task that needs to be investigated, and the Disguised Faces in the Wild (DFW) competition is designed for this task. In this paper, we propose a two-stage training approach to utilize the small-scale training data provided by the DFW competition. Specifically, in the first stage, we train Deep Convolutional Neural Networks (DCNNs) for generic face recognition. In the second stage, we use Principal Components Analysis (PCA) based on the DFW training set to find the best transformation matrix for identity representation of disguised faces. We evaluate our model on the DFW testing dataset and it shows better performance over the state-of-the-art generic face recognition methods. It also achieves the best results on the DFW competition - Phase 1.

关键词： Face recognition Training Testing Feature extraction Principal component analysis Training data Pipelines

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Semantic Binary Segmentation using Convolutional Networks without Decoders 31

Semantic Binary Segmentation using Convolutional Networks wi...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Aich, Shubhra van der Kamp, William Stavness, Ian Univ Saskatchewan Dept Comp Sci Saskatoon SK Canada

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In this paper, we propose an efficient architecture for semantic image segmentation using the depth-to-space (D2S) operation. Our D2S model is comprised of a standard CNN encoder followed by a depth-to-space reordering of the final convolutional feature maps. Our approach eliminates the decoder portion of traditional encoder-decoder segmentation models and reduces the amount of computation almost by half. As a participant of the DeepGlobe Road Extraction competition, we evaluate our models on the corresponding road segmentation dataset. Our highly efficient D2S models exhibit comparable performance to standard segmentation models with much lower computational cost.

关键词： Image segmentation Decoding Convolution computer architecture Computational modeling Roads Semantics

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