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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

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Road detection with EOSResUNet and post vectorizing algorithm 31

Road detection with EOSResUNet and post vectorizing algorith...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Filin, Oleksandr Zapara, Anton Panchenko, Serhii EOS Data Analyt Menlo Pk CA 94027 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Object recognition on the satellite images is one of the most relevant and popular topics in the problem of pattern recognition. This was facilitated by many factors, such as a high number of satellites with high-resolution imagery, the significant development of computer vision, especially with a major breakthrough in the field of convolutional neural networks, a wide range of industry verticals for usage and still a quite empty market. Roads are one of the most popular objects for recognition. In this article, we want to present you the combination of work of neural network and postprocessing algorithm, due to which we get not only the coverage mask but also the vectors of all of the individual roads that are present in the image and can be used to address the higher-level tasks in the future. This approach was used to solve the DeepGlobe Road Extraction Challenge.

关键词： Roads Neural networks Task analysis Satellites computer vision Earth Observing System Data analysis

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Improved Cooperative Stereo Matching for Dynamic vision Sensors with Ground Truth Evaluation 30

Improved Cooperative Stereo Matching for Dynamic Vision Sens...

引用

30th ieee/cvf conference on computer vision and pattern recognition workshops (cvprw)

作者： Piatkowska, Ewa Kogler, Juergen Belbachir, Ahmed Nabil Gelautz, Margrit AIT Vienna Austria Vienna Univ Technol Vienna Austria Teknova AS Grimstad Norway

ISBN: (纸本)9781538607336

Event-based vision, as realized by bio-inspired Dynamic vision Sensors (DVS), is gaining more and more popularity due to its advantages of high temporal resolution, wide dynamic range and power efficiency at the same time. Potential applications include surveillance, robotics, and autonomous navigation under uncontrolled environment conditions. In this paper, we deal with event-based vision for 3D reconstruction of dynamic scene content by using two stationary DVS in a stereo configuration. We focus on a cooperative stereo approach and suggest an improvement over a previously published algorithm that reduces the measured mean error by over 50 percent. An available ground truth data set for stereo event data is utilized to analyze the algorithm's sensitivity to parameter variation and for comparison with competing techniques.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Conjugate Adder Net (CAddNet) - a Space-Efficient Approximate CNN

Conjugate Adder Net (CAddNet) - a Space-Efficient Approximat...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Shen, Lulan Ziaeefard, Maryam Meyer, Brett Gross, Warren Clark, James J. McGill Univ Montreal PQ Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The AdderNet was recently developed as a way to implement deep neural networks without needing multiplication operations to combine weights and inputs. Instead, absolute values of the difference between weights and inputs are used, greatly reducing the gate-level implementation complexity. Training of AdderNets is challenging, however, and the loss curves during training tend to fluctuate significantly. In this paper we propose the Conjugate Adder Network, or CAddNet, which uses the difference between the absolute values of conjugate pairs of inputs and the weights. We show that this can be implemented simply via a single minimum operation, resulting in a roughly 50% reduction in logic gate complexity as compared with AdderNets. The CAddNet method also stabilizes training as compared with AdderNets, yielding training curves similar to standard CNNs.

关键词： Training Deep learning conferences Neural networks Logic gates Complexity theory pattern recognition

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Filter Distribution Templates in Convolutional Networks for Image Classification Tasks

Filter Distribution Templates in Convolutional Networks for ...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Izquierdo-Cordova, Ramon Mayol-Cuevas, Walterio Univ Bristol Dept Comp Sci Bristol Avon England

ISBN: (纸本)9781665448994

Neural network designers have reached progressive accuracy by increasing models depth, introducing new layer types and discovering new combinations of layers. A common element in many architectures is the distribution of the number of filters in each layer. Neural network models keep a pattern design of increasing filters in deeper layers such as those in LeNet, VGG, ResNet, MobileNet and even in automatic discovered architectures such as NASNet. It remains unknown if this pyramidal distribution of filters is the best for different tasks and constrains. In this work we present a series of modifications in the distribution of filters in three popular neural network models and their effects in accuracy and resource consumption. Results show that by applying this approach, some models improve up to 8.9% in accuracy showing reductions in parameters up to 54%.

关键词： Computational modeling Neural networks Memory management Manuals Data models pattern recognition Convolutional neural networks

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Feedback control of event cameras

Feedback control of event cameras

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Delbruck, Tobi Graca, Rui Paluch, Marcin UZH ETH Zurich Inst Neuroinformat Zurich Switzerland

ISBN: (纸本)9781665448994

Dynamic vision sensor event cameras produce a variable data rate stream of brightness change events. Event production at the pixel level is controlled by threshold, bandwidth, and refractory period bias current parameter settings. Biases must be adjusted to match application requirements and the optimal settings depend on many factors. As a first step towards automatic control of biases, this paper proposes fixed-step feedback controllers that use measurements of event rate and noise. The controllers regulate the event rate within an acceptable range using threshold and refractory period control, and regulate noise using bandwidth control. Experiments demonstrate model validity and feedback control.

关键词： computer vision Current measurement conferences Bandwidth Production vision sensors Cameras

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ReMP: Rectified Metric Propagation for Few-Shot Learning

ReMP: Rectified Metric Propagation for Few-Shot Learning

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Zhao, Yang Li, Chunyuan Yu, Ping Chen, Changyou Univ Buffalo Buffalo NY 14260 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781665448994

Few-shot learning features the capability of generalizing from a few examples. In this paper, we first identify that a discriminative feature space, namely a rectified metric space, that is learned to maintain the metric consistency from training to testing, is an essential component to the success of metric-based few-shot learning. Numerous analyses indicate that a simple modification of the objective can yield substantial performance gains. The resulting approach, called rectified metric propagation (ReMP), further optimizes an attentive prototype propagation network, and applies a repulsive force to make confident predictions. Extensive experiments demonstrate that the proposed ReMP is effective and efficient, and outperforms the state of the arts on various standard few-shot learning datasets.

关键词： Training computer vision conferences Force Prototypes Performance gain Extraterrestrial measurements

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Three Gaps for Quantisation in Learned Image Compression

Three Gaps for Quantisation in Learned Image Compression

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Pan, Shi Finlay, Chris Besenbruch, Chri Knottenbelt, William Imperial Coll London Dept Comp London England DeepRender London England

ISBN: (纸本)9781665448994

Learned lossy image compression has demonstrated impressive progress via end-to-end neural network training. However, this end-to-end training belies the fact that lossy compression is inherently not differentiable, due to the necessity of quantisation. To overcome this difficulty in training, researchers have used various approximations to the quantisation step. However, little work has studied the mechanism of quantisation approximation itself. We address this issue, identifying three gaps arising in the quantisation approximation problem. These gaps are visualised, and show the effect of applying different quantisation approximation methods. Following this analysis, we propose a Soft-STE quantisation approximation method, which closes these gaps and demonstrates better performance than other quantisation approaches on the Kodak dataset.

关键词： Training Visualization computer vision Quantization (signal) Image coding conferences Neural networks

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Perceptual Image Compression using Relativistic Average Least Squares GANs

Perceptual Image Compression using Relativistic Average Leas...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Cheng, Zhengxue Fu, Ting Hu, Jiapeng Guo, Li Wang, Shihao Zhao, Xiongxin Zhou, Dajiang Song, Yang Ant Grp Hangzhou Peoples R China

ISBN: (纸本)9781665448994

In this work, we provide a detailed description on our submitted methods ANTxNN and ANTxNN SSIM to Workshop and Challenge on Learned Image Compression (CLIC) 2021. We propose to incorporate Relativistic average Least Squares GANs (RaLSGANs) into Rate-Distortion Optimization for end-to-end training, to achieve perceptual image compression. We also compare two types of discriminator networks and visualize their reconstructed images. Experimental results have validated our method optimized by RaLSGANs can achieve higher subjective quality compared to PSNR, MS-SSIM or LPIPS-optimized models.

关键词： Training Visualization computer vision Image coding conferences Rate-distortion Generative adversarial networks

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Simplifying Transformations for a Family of Elastic Metrics on the Space of Surfaces

Simplifying Transformations for a Family of Elastic Metrics ...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Su, Zhe Bauer, Martin Klassen, Eric Gallivan, Kyle Florida State Univ Dept Math Tallahassee FL 32306 USA

ISBN: (纸本)9781728193601

We define a new representation for immersed surfaces in R-3 by combining the SRNF and the induced surface metric. Using the L-2 metric on the space of SRNFs and the DeWitt metric on the space of surface metrics, we obtain a 3-parameter family of metrics that corresponds to the family of "elastic metrics" proposed by Jermyn et al. in [19] on the space of immersed surfaces. Similar to the original SRNF representation this new representation results in an extrinsic distance function on the space of immersed surfaces that is easy to compute as it is given by an explicit formula. In addition to avoiding the degeneracy of the SRNF it allows for a data-driven choice of the parameters of the metric, while still providing for fast and accurate registration of surfaces.

关键词： computer vision

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Sample-free white-box out-of-distribution detection for deep learning

Sample-free white-box out-of-distribution detection for deep...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Begon, Jean-Michel Geurts, Pierre Univ Liege Liege Belgium

ISBN: (纸本)9781665448994

Being able to detect irrelevant test examples with respect to deployed deep learning models is paramount to properly and safely using them. In this paper, we address the problem of rejecting such out-of-distribution (OOD) samples in a fully sample-free way, i.e., without requiring any access to in-distribution or OOD samples. We propose several indicators which can be computed alongside the prediction with little additional cost, assuming white-box access to the network. These indicators prove useful, stable and complementary for OOD detection on frequently-used architectures. We also introduce a surprisingly simple, yet effective summary OOD indicator. This indicator is shown to perform well across several networks and datasets and can furthermore be easily tuned as soon as samples become available. Lastly, we discuss how to exploit this summary in real-world settings.

关键词： Deep learning computer vision Filtering Computational modeling conferences computer architecture Data models

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