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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"

共 3320 条记录，以下是211-220 订阅

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Multimodal Shape Completion via Implicit Maximum Likelihood Estimation

Multimodal Shape Completion via Implicit Maximum Likelihood ...

引用

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

作者： Arora, Himanshu Mishra, Saurabh Peng, Shichong Li, Ke Mahdavi-Amiri, Ali Simon Fraser Univ Burnaby BC Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Shape completion is the problem of completing partial input shapes such as partial scans. This problem finds important applications in computer vision and robotics due to issues such as occlusion or sparsity in real-world data. However, most of the existing research related to shape completion has been focused on completing shapes by learning a one-to-one mapping which limits the diversity and creativity of the produced results. We propose a novel multimodal shape completion technique that is effectively able to learn a one-to-many mapping and generates diverse complete shapes. Our approach is based on the conditional Implicit Maximum Likelihood Estimation (IMLE) technique wherein we condition our inputs on partial 3D point clouds. We extensively evaluate our approach by comparing it to various baselines both quantitatively and qualitatively. We show that our method is superior to alternatives in terms of completeness and diversity of shapes.

关键词： Point cloud compression Maximum likelihood estimation computer vision Three-dimensional displays Shape conferences pattern recognition

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When Kernel Methods meet Feature Learning: Log-Covariance Network for Action recognition from Skeletal Data 30

When Kernel Methods meet Feature Learning: Log-Covariance Ne...

引用

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

作者： Cavazza, Jacopo Morerio, Pietro Murino, Vittorio Ist Italiano Tecnol Pattern Anal & Comp Vis PAVIS Genoa Italy Univ Genoa Electr Elect & Telecommun Engn Naval Architecture Genoa Italy Univ Verona Comp Sci Dept Verona Italy

ISBN: (纸本)9781538607336

Human action recognition from skeletal data is a hot research topic and important in many open domain applications of computer vision, thanks to recently introduced 3D sensors. In the literature, naive methods simply transfer off-the-shelf techniques from video to the skeletal representation. However, the current state-of-the-art is contended between to different paradigms: kernel-based methods and feature learning with (recurrent) neural networks. Both approaches show strong performances, yet they exhibit heavy, but complementary, drawbacks. Motivated by this fact, our work aims at combining together the best of the two paradigms, by proposing an approach where a shallow network is fed with a covariance representation. Our intuition is that, as long as the dynamics is effectively modeled, there is no need for the classification network to be deep nor recurrent in order to score favorably. We validate this hypothesis in a broad experimental analysis over 6 publicly available datasets.

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

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Manipulation Detection in Satellite Images Using vision Transformer

Manipulation Detection in Satellite Images Using Vision Tran...

引用

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

作者： Horvath, Janos Baireddy, Sriram Hao, Hanxiang Montserrat, Daniel Mas Delp, Edward J. Purdue Univ Sch Elect & Comp Engn Video & Image Proc Lab VIPER W Lafayette IN 47907 USA

ISBN: (纸本)9781665448994

A growing number of commercial satellite companies provide easily accessible satellite imagery. Overhead imagery is used by numerous industries including agriculture, forestry, natural disaster analysis, and meteorology. Satellite images, just as any other images, can be tampered with image manipulation tools. Manipulation detection methods created for images captured by "consumer cameras" tend to fail when used on satellite images due to the differences in image sensors, image acquisition, and processing. In this paper we propose an unsupervised technique that uses a vision Transformer to detect spliced areas within satellite images. We introduce a new dataset which includes manipulated satellite images that contain spliced objects. We show that our proposed approach performs better than existing unsupervised splicing detection techniques.

关键词： Image sensors computer vision Satellites Splicing conferences Forestry Tools

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Occlusion Guided Scene Flow Estimation on 3D Point Clouds

Occlusion Guided Scene Flow Estimation on 3D Point Clouds

引用

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

作者： Ouyang, Bojun Raviv, Dan Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9781665448994

3D scene flow estimation is a vital tool in perceiving our environment given depth or range sensors. Unlike optical flow, the data is usually sparse and in most cases partially occluded in between two temporal samplings. Here we propose a new scene flow architecture called OGSF-Net which tightly couples the learning for both flow and occlusions between frames. Their coupled symbiosis results in a more accurate prediction of flow in space. Unlike a traditional multi-action network, our unified approach is fused throughout the network, boosting performances for both occlusion detection and flow estimation. Our architecture is the first to gauge the occlusion in 3D scene flow estimation on point clouds. In key datasets such as Flyingthings3D and KITTI, we achieve the state-of-the-art results.(1,2)

关键词： Symbiosis Measurement Deep learning Three-dimensional displays Estimation computer architecture Tools

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Continual Hippocampus Segmentation with Transformers

Continual Hippocampus Segmentation with Transformers

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

作者： Ranem, Amin Gonzalez, Camila Mukhopadhyay, Anirban Tech Univ Darmstadt GRIS Karolinenpl 5 D-64289 Darmstadt Germany

ISBN: (纸本)9781665487399

In clinical settings, where acquisition conditions and patient populations change over time, continual learning is key for ensuring the safe use of deep neural networks. Yet most existing work focuses on convolutional architectures and image classification. Instead, radiologists prefer to work with segmentation models that outline specific regions-of-interest, for which Transformer-based architectures are gaining traction. The self-attention mechanism of Transformers could potentially mitigate catastrophic forgetting, opening the way for more robust medical image segmentation. In this work, we explore how recently-proposed Transformer mechanisms for semantic segmentation behave in sequential learning scenarios, and analyse how best to adapt continual learning strategies for this setting. Our evaluation on hippocampus segmentation shows that Transformer mechanisms mitigate catastrophic forgetting for medical image segmentation compared to purely convolutional architectures, and demonstrates that regularising ViT modules should be done with caution.

关键词： Image segmentation Semantics Sociology computer architecture Artificial neural networks Transformers Task analysis

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DarkLight Networks for Action recognition in the Dark

DarkLight Networks for Action Recognition in the Dark

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

作者： Chen, Rui Chen, Jiajun Liang, Zixi Gao, Huaien Lin, Shan Guangzhou Xi Ma Informat Technol Co 101 Waihuan Xi Rd Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9781665448994

Human action recognition in the dark is a significant task with various applications, e.g., night surveillance and self-driving at night. However, the lack of video datasets for human actions in the dark hinders its development. Recently, a public dataset ARID has been introduced to stimulate progress for the task of human action recognition in dark videos. Currently, there are multiple models that perform well for action recognition in videos shot under normal illumination. However, research shows that these methods may not be effective in recognizing actions in dark videos. In this paper, we construct a novel neural network architecture: DarkLight Networks, which involves (i) a dual-pathway structure where both dark videos and its brightened counterpart are utilized for effective video representation;and (ii) a self-attention mechanism, which fuses and extracts corresponding and complementary features from the two pathways. Our approach achieves state-of-the-art results on ARID.

关键词： computer vision Fuses Surveillance conferences Neural networks Lighting Feature extraction

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Two-stage Network For Single Image Super-Resolution

Two-stage Network For Single Image Super-Resolution

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

作者： Han, Yuzhuo Du, Xiaobiao Yang, Zhi Dalian Univ Technol Dalian Peoples R China Jilin Univ Zhuhai Coll Zhuhai Peoples R China Dibaocheng Shanghai Med Imaging Technol Co Ltd Shanghai Peoples R China

ISBN: (纸本)9781665448994

The task of single-image super-resolution (SISR) is a highly inverse problem because it is very challenging to reconstruct rich details from blurred images. Most previous super-resolution (SR) methods based on the convolutional neural networks (CNN) tend to design more complex network structures to directly learn the mapping between low-resolution images and high-resolution images. However, this is not the best choice to blindly increase the network depth, because the performance improvement may not increase, but it will increase the computational cost. To solve this problem, we propose an effective method that learns high frequency information in high-resolution images to enhance the image reconstruction. In this work, we propose a two-stage network (TSN) to recover clear SR images. The proposed TSN firstly learns the high frequency information in high-resolution images, then learns how to transform to high-resolution images. A large number of experiments show that our TSN achieves satisfactory performance.

关键词： computer vision Inverse problems conferences Computational modeling Superresolution Transforms pattern recognition

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A Deeper Look into Aleatoric and Epistemic Uncertainty Disentanglement

A Deeper Look into Aleatoric and Epistemic Uncertainty Disen...

引用

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

作者： Valdenegro-Toro, Matias Mori, Daniel Saromo Univ Groningen Dept AI Bernoulli Inst Groningen Netherlands Pontifical Catholic Univ Peru Artificial Intelligence Res Grp San Miguel Peru

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Neural networks are ubiquitous in many tasks, but trusting their predictions is an open issue. Uncertainty quantification is required for many applications, and disentangled aleatoric and epistemic uncertainties are best. In this paper, we generalize methods to produce disentangled uncertainties to work with different uncertainty quantification methods, and evaluate their capability to produce disentangled uncertainties. Our results show that: there is an interaction between learning aleatoric and epistemic uncertainty, which is unexpected and violates assumptions on aleatoric uncertainty, some methods like Flipout produce zero epistemic uncertainty, aleatoric uncertainty is unreliable in the out-of-distribution setting, and Ensembles provide overall the best disentangling quality. We also explore the error produced by the number of samples hyper-parameter in the sampling softmax function, recommending N > 100 samples. We expect that our formulation and results help practitioners and researchers choose uncertainty methods and expand the use of disentangled uncertainties, as well as motivate additional research into this topic.

关键词： computer vision Uncertainty conferences Neural networks pattern recognition Task analysis

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vision + Language Applications: A Survey

Vision + Language Applications: A Survey

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Zhou, Yutong Shimada, Nobutaka Ritsumeikan University Shiga Japan

ISBN: (纸本)9798350302493

Text-to-image generation has attracted significant interest from researchers and practitioners in recent years due to its widespread and diverse applications across various industries. Despite the progress made in the domain of vision and language research, the existing literature remains relatively limited, particularly with regard to advancements and applications in this field. This paper explores a relevant research track within multimodal applications, including text, vision, audio, and others. In addition to the studies discussed in this paper, we are also committed to continually updating the latest relevant papers, datasets, application projects and corresponding information at https://***/Yutong-Zhou-cv/Awesome-Text-to-Image. © 2023 ieee.

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DeepRing: Protecting Deep Neural Network with Blockchain 32

DeepRing: Protecting Deep Neural Network with Blockchain

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32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Goel, Akhil Agarwal, Akshay Vatsa, Mayank Singh, Richa Ratha, Nalini IIIT Delhi Delhi NY 13753 USA IBM Res Ossining NY USA

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125060

Several computer vision applications such as object detection and face recognition have started to completely rely on deep learning based architectures. These architectures, when paired with appropriate loss functions and optimizers, produce state-of-the-art results in a myriad of problems. On the other hand, with the advent of "blockchain", the cybersecurity industry has developed a new sense of trust which was earlier missing from both the technical and commercial perspectives. Employment of cryptographic hash, as well as symmetric/asymmetric encryption and decryption algorithms, ensure security without any human intervention (i.e., centralized authority). In this research, we present the synergy between the best of both these worlds. We first propose a model which uses the learned parameters of a typical deep neural network and is secured from external adversaries by cryptography and blockchain technology. As the second contribution of the proposed research, a new parameter tampering attack is proposed to properly justify the role of blockchain in machine learning.

关键词： Public key computer architecture Machine learning

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