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检索条件"任意字段=2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022"

共 3917 条记录，以下是3521-3530 订阅

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Simultaneous Identification and Tracking of Multiple People using Video and IMUs 32

Simultaneous Identification and Tracking of Multiple People ...

引用

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

作者： Henschel, Roberto von Marcard, Timo Rosenhahn, Bodo Leibniz Univ Hannover Hannover Germany

ISBN: (纸本)9781728125060

Most modern approaches for multiple people tracking rely on human appearance to exploit similarity between person detections. In this work, we propose an alternative tracking method that does not depend on visual appearance and is still capable to deal with very dynamic motions and long-term occlusions. We make this feasible by: (i) incorporating additional information from body-worn inertial sensors, (ii) designing a neural network to relate person detections to orientation measurements and (iii) formulating a graph labeling problem to obtain a tracking solution that is globally consistent with the video and inertial recordings. We evaluate our approach on several challenging tracking sequences and achieve a very high IDF1 score of 91.2%. We outperform appearance-based baselines in scenarios where appearance is less informative and are on-par in situations with discriminative people appearance.

关键词： computer vision

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SIDOD: A Synthetic Image Dataset for 3D Object Pose recognition with Distractors 32

SIDOD: A Synthetic Image Dataset for 3D Object Pose Recognit...

引用

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

作者： Jalal, Mona Spjut, Josef Boudaoud, Ben Betke, Margrit Boston Univ Boston MA 02215 USA NVIDIA Raleigh NC USA

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125060

We present a new, publicly-available image dataset generated by the NVIDIA Deep Learning Data Synthesizer intended for use in object detection, pose estimation, and tracking applications. This dataset contains 144k stereo image pairs that synthetically combine 18 camera viewpoints of three photorealistic virtual environments with up to 10 objects (chosen randomly from the 21 object models of the YCB dataset [1]) and flying distractors. Object and camera pose, scene lighting, and quantity of objects and distractors were randomized. Each provided view includes RGB, depth, segmentation, and surface normal images, all pixel level. We describe our approach for domain randomization and provide insight into the decisions that produced the dataset.

关键词： Three-dimensional displays Pose estimation Cameras Image segmentation computer vision Training Lighting

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Surrogate Contrastive Network for Supervised Band Selection in Multispectral computer vision Tasks 32

Surrogate Contrastive Network for Supervised Band Selection ...

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

作者： Bernal, Edgar A. Univ Rochester Rochester Data Sci Consortium 260 E Main St Rochester NY 14604 USA

ISBN: (纸本)9781728125060

computer vision techniques that operate on hyper- and multispectral imagery benefit from the additional amount of spectral information relative to those that exploit traditional RGB or monochromatic visual data. However, the increased volume of data to be processed brings about additional memory, storage and computational requirements. In order to address such limitations, a wide range of techniques for dimensionality reduction have been introduced by previous work. In this paper, we propose a framework for spectral band selection that is highly data- and computationally efficient. The method leverages a convolutional siamese network learned by optimizing a contrastive loss, and performs band selection based on the low-dimensional data embeddings produced by the network. We empirically demonstrate the efficacy of the method on an object detection task from aerial multispectral imagery. The results show that, in spite of the method's frugality, it produces very competitive band selection results against the evaluated competing techniques.

关键词： Object detection

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WiCV 2019: The Sixth Women In computer vision Workshop 32

WiCV 2019: The Sixth Women In Computer Vision Workshop

引用

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

作者： Amerini, Irene Balashova, Elena Ebrahimi, Sayna Leonard, Kathryn Nagrani, Arsha Salvador, Amaia Univ Florence Florence Italy Princeton Univ Princeton NJ 08544 USA Univ Calif Berkeley Berkeley CA USA Occident Coll Los Angeles CA USA Univ Oxford Oxford England Univ Politen Catalunya Barcelona Spain

ISBN: (纸本)9781728125060

In this paper we present the Women in computer vision Workshop - WiCV 2019, organized in conjunction with CVPR 2019. This event is meant for increasing the visibility and inclusion of women researchers in computer vision field. computer vision and machine learning have made incredible progress over the past years, but the number of female researchers is still low both in the academia and in the industry. WiCV is organized especially for this reason: to raise visibility of female researchers, to increase collaborations between them, and to provide mentorship to female junior researchers in the field. In this paper, we present a report of trends over the past years, along with a summary of statistics regarding presenters, attendees, and sponsorship for the current workshop.

关键词： computer vision

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Towards computer vision powered color-nutrient assessment of pureed food 32

Towards computer vision powered color-nutrient assessment of...

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

作者： Pfisterer, Kaylen J. Amelard, Robert Syrnyk, Braeden Wong, Alexander Univ Waterloo Vis & Image Proc Res Grp Waterloo ON Canada Schlegel UW Res Inst Aging Waterloo ON Canada

ISBN: (纸本)9781728125060

With one in four individuals afflicted with malnutrition, computer vision may provide a way of introducing a new level of automation in the nutrition field to reliably monitor food and nutrient intake. In this study, we present a novel approach to modeling the link between color and vitamin A content using transmittance imaging of a pureed foods dilution series in a computer vision powered nutrient sensing system via a fine-tuned deep autoencoder network, which in this case was trained to predict the relative concentration of sweet potato purees. Experimental results show the deep autoencoder network can achieve an accuracy of 80% across beginner (6 month) and intermediate (8 month) commercially prepared pureed sweet potato samples. Prediction errors may be explained by fundamental differences in optical properties which are further discussed.

关键词： Nutrients

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Learning Personal Tastes in Choosing Fashion Outfits 32

Learning Personal Tastes in Choosing Fashion Outfits

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

作者： Lin, Yusan Moosaei, Maryam Yang, Hao Visa Res Palo Alto CA 94306 USA

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125060

With the emergence of fashion recommendation, many researchers have attempted to recommend fashion items that fit consumers' tastes. However, few have looked into fashion outfits as a whole when making recommendations. In this paper, we propose a neural network that learns one's fashion taste and predicts whether an individual likes a fashion outfit. To improve learning, we also develop a fashion outfit negative sampling scheme to sample fashion outfits that are different enough. With experiments on the collected Polyvore dataset, we find that using complete images offashion outfits performs well when learning individuals' tastes toward fashion outfits. Our proposed negative sampling scheme also improves the model's performance significantly, compared to random negative sampling.

关键词： Neural networks Predictive models Visualization Data models computer vision conferences Ions

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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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Anticipation of Human Actions with Pose-based Fine-grained Representations 32

Anticipation of Human Actions with Pose-based Fine-grained R...

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

作者： Agethen, Sebastian Lee, Hu-Cheng Hsu, Winston H. Natl Taiwan Univ Taipei Taiwan

ISBN: (纸本)9781728125060

Anticipating an action that is about to happen allows us to be more efficient in interacting with our environment. However, prediction is a challenging task in computer vision, because videos are only partially available when a decision is to be made. Complicating the issue is that it is not always clear which of the visible activities in the scene are relevant to the action, and which ones are not. We suggest that the key to recognizing an action lies with the human actors, and that it is therefore necessary for the prediction process to attend to persons in a scene. In our work, we extract fine-grained features on visible human actors and predict the future via an L2-regression in feature space. This allows the regressed future feature to focus on the actor. Using this, the future action is classified. More specifically, the fine-grained extraction is guided by a pose prediction system that models current and future human poses in the scene. We run qualitative and quantitative experiments on the Charades dataset, and initial results show that our system improves action prediction.

关键词： computer vision

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Measuring the Effects of Temporal Coherence in Depth Estimation for Dynamic Scenes 32

Measuring the Effects of Temporal Coherence in Depth Estimat...

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

作者： Tsekourakis, Iraklis Mordohai, Philippos Stevens Inst Technol Hoboken NJ 07030 USA

ISBN: (纸本)9781728125060

This paper presents a new algorithm for enforcing temporal coherence on depth estimation from multi-view videos of dynamic scenes as well as the first substantial quantitative evaluation of the improvement in depth estimation accuracy due to temporal coherence. The proposed algorithm is generally applicable and practical since it bypasses explicit scene flow estimation, which has a very large state space, and relies only on optical flow which is used to impose soft constraints on depth estimation for the next frame. As a result, our algorithm is applicable to scenes with large depth and motion ranges. The output is a sequence of depth maps that can be used for novel view synthesis among other applications. While it is intuitive that enforcing temporal coherence should improve the accuracy of depth estimation, this improvement has never been assessed quantitatively due to the lack of data with ground truth. To overcome this limitation we use the image prediction error as the criterion and show that the benefits of temporal coherence are significant on a diverse set of multi-view video sequences.

关键词： computer vision

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Improving GAN Equilibrium by Raising Spatial Awareness

Improving GAN Equilibrium by Raising Spatial Awareness

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conference on computer vision and pattern recognition (CVPR)

作者： Jianyuan Wang Ceyuan Yang Yinghao Xu Yujun Shen Hongdong Li Bolei Zhou The Chinese University of Hong Kong The Australian National University University of California Los Angeles

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469470

The success of Generative Adversarial Networks (GANs) is largely built upon the adversarial training between a generator (G) and a discriminator (D). They are expected to reach a certain equilibrium where D cannot distinguish the generated images from the real ones. However, such an equilibrium is rarely achieved in practical GAN training, instead, D almost always surpasses G. We attribute one of its sources to the information asymmetry between D and G. We observe that D learns its own visual attention when determining whether an image is real or fake, but G has no explicit clue on which regions to focus on for a particular synthesis. To alleviate the issue of D dominating the competition in GANs, we aim to raise the spatial awareness of G. Randomly sampled multi-level heatmaps are encoded into the intermediate layers of G as an inductive bias. Thus G can purposefully improve the synthesis of certain image regions. We further propose to align the spatial awareness of G with the attention map induced from D. Through this way we effectively lessen the information gap between D and G. Extensive results show that our method pushes the two-player game in GANs closer to the equilibrium, leading to a better synthesis performance. As a byproduct, the intro-duced spatial awareness facilitates interactive editing over the output synthesis. Demo video and code are available at https://***/eqgan-sa/

关键词： Training Heating systems Visualization computer vision Image synthesis Computational modeling Games

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