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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"

共 6678 条记录，以下是911-920 订阅

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Long-term Head Pose Forecasting Conditioned on the Gaze-guiding Prior

Long-term Head Pose Forecasting Conditioned on the Gaze-guid...

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

作者： Mo, Shentong Xin, Miao Carnegie Mellon Univ Pittsburgh PA 15213 USA Chinese Acad Sci Inst Automat CASIA Beijing Peoples R China

ISBN: (纸本)9781665448994

Forecasting head pose future states is a novel task in computer vision. Since future may have many possibilities, and the logical results are much more important than the impractical ones, the forecasting results for most of the scenarios should be not only diverse but also logically realistic. These requirements pose a real challenge to the current methods, which motivates us to seek for better head pose representation and methods to restrict the forecasting reasonably. In this paper, we adopt a spatial-temporal graph to model the interdependencies between the distribution of landmarks and head pose angles. Furthermore, we propose the conditional spatial-temporal variational graph autoencoder (CST-VGAE), a deep conditional generative model for learning restricted one-to-many mappings conditioned on the spatial-temporal graph input. Specifically, we improve the proposed CST-VGAE for the long-term head pose forecasting task in terms of several aspects. First, we introduce a gaze-guiding prior based on the physiology. Then we apply a temporal self-attention and self-supervised learning mechanism to learn the long-range dependencies on the gaze prior. To better model head poses structurally, we introduce a Gaussian Mixture Model (GMM), instead of a fixed Gaussian in the encoded latent space. Experiments demonstrate the effectiveness of the proposed method for the long-term head pose forecasting task. We achieve superior forecasting performance on the benchmark datasets compared to the existing methods.

关键词： Learning systems computer vision conferences Predictive models Benchmark testing Physiology pattern recognition

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One-shot action recognition in challenging therapy scenarios

One-shot action recognition in challenging therapy scenarios

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

作者： Sabater, Alberto Santos, Laura Santos-Victor, Jose Bernardino, Alexandre Montesano, Luis Murillo, Ana C. Univ Lisbon Inst Super Tecn ISR Lisboa Lisbon Portugal Univ Zaragoza DIIS I3A Zaragoza Spain Bitbrain Technol Zaragoza Spain Politecn Milan Milan Italy

ISBN: (纸本)9781665448994

One-shot action recognition aims to recognize new action categories from a single reference example, typically referred to as the anchor example. This work presents a novel approach for one-shot action recognition in the wild that computes motion representations robust to variable kinematic conditions. One-shot action recognition is then performed by evaluating anchor and target motion representations. We also develop a set of complementary steps that boost the action recognition performance in the most challenging scenarios. Our approach is evaluated on the public NTU-120 one-shot action recognition benchmark, outperforming previous action recognition models. Besides, we evaluate our framework on a real use-case of therapy with autistic people. These recordings are particularly challenging due to high-level artifacts from the patient motion. Our results provide not only quantitative but also online qualitative measures, essential for the patient evaluation and monitoring during the actual therapy.

关键词： Target recognition conferences Dynamics Medical treatment Kinematics pattern recognition Noise measurement

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ABAW: Valence-Arousal Estimation, Expression recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challenges

ABAW: Valence-Arousal Estimation, Expression Recognition, Ac...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Dimitrios Kollias Panagiotis Tzirakis Alice Baird Alan Cowen Stefanos Zafeiriou Queen Mary University of London UK Hume AI USA Imperial College London UK

The 5th ABAW Competition is part of the respective Workshop held in conjunction with ieee cvpr 2023 and is a continuation of the Competitions held at ECCV 2022, ieee cvpr 2022, ICCV 2021, ieee FG 2020 and cvpr 2017 conferences. It is dedicated at automatically analyzing affect. For this year’s Competition, we feature two corpora: i) an extended version of the Aff-Wild2 database and ii) the Hume-Reaction dataset. The former database is an audiovisual (A/V) one of around 600 videos of around 3M frames and is annotated for: a) two continuous affect dimensions, valence (how positive/negative a person is) and arousal (how active/passive a person is); b) basic expressions (e.g. happiness, neutral); and c) action units (i.e., facial muscle actions). The latter dataset is A/V in which reactions of individuals to emotional stimuli have been annotated for seven emotional expression intensities. Thus the 5th ABAW Competition encompasses four Challenges: i) Valence-Arousal Estimation, ii) Expression Classification, iii) Action Unit Detection, and iv) Emotional Reaction Intensity Estimation. In this paper, we present these Challenges and their corpora, we outline the evaluation metrics and present the baseline systems and top performing teams’ per Challenge along with their obtained performance. More information for the Competition can be found in: https://***/resources/ cvpr-2023-5th-abaw.

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Moment Centralization-Based Gradient Descent Optimizers for Convolutional Neural Networks 1

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International conference on computer vision and Machine Intelligence, CVMI 2022

作者： Sadu, Sumanth Dubey, Shiv Ram Sreeja, S.R. Computer Vision Group Department of Computer Science and Engineering Indian Institute of Information Technology Andhra Pradesh Sri City India Computer Vision and Biometrics Laboratory Indian Institute of Information Technology Uttar Pradesh Allahabad India Department of Computer Science and Engineering Indian Institute of Information Technology Andhra Pradesh Sri City India

ISBN: (数字)9789811978678

ISBN: (纸本)9789811978661

Convolutional neural networks (CNNs) have shown very appealing performance for many computer vision applications. The training of CNNs is generally performed using stochastic gradient descent (SGD)-based optimization techniques. The adaptive momentum-based SGD optimizers are the recent trends. However, the existing optimizers are not able to maintain a zero mean in the first order moment and struggle with optimization. In this paper, we propose a moment centralization-based SGD optimizer for CNNs. Specifically, we impose the zero mean constraints on the first order moment explicitly. The proposed moment centralization is generic in nature and can be integrated with any of the existing adaptive momentum-based optimizers. The proposed idea is tested with three state-of-the-art optimization techniques, including Adam, Radam, and Adabelief on benchmark CIFAR10, CIFAR100, and TinyImageNet datasets for image classification. The performance of the existing optimizers is generally improved when integrated with the proposed moment centralization. Further, the results of the proposed moment centralization are also better than the existing gradient centralization. The analytical analysis using the toy example shows that the proposed method leads to a shorter and smoother optimization trajectory. The source code is made publicly available at https://***/sumanthsadhu/MC-optimizer. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optimization

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Fast Walsh-Hadamard Transform and Smooth-Thresholding Based Binary Layers in Deep Neural Networks

Fast Walsh-Hadamard Transform and Smooth-Thresholding Based ...

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

作者： Pan, Hongyi Badawi, Diaa Cetin, Ahmet Enis Univ Illinois Dept Elect & Comp Engn Chicago IL 60680 USA

ISBN: (纸本)9781665448994

In this paper, we propose a novel layer based on fast Walsh-Hadamard transform (WHT) and smooth-thresholding to replace 1 x 1 convolution layers in deep neural networks. In the WHT domain, we denoise the transform domain coefficients using the new smooth-thresholding non-linearity, a smoothed version of the well-known soft-thresholding operator. We also introduce a family of multiplication-free operators from the basic 2x2 Hadamard transform to implement 3 x 3 depthwise separable convolution layers. Using these two types of layers, we replace the bottleneck layers in MobileNet-V2 to reduce the network's number of parameters with a slight loss in accuracy. For example, by replacing the final third bottleneck layers, we reduce the number of parameters from 2.270M to 947K. This reduces the accuracy from 95.21% to 92.88% on the CIFAR-10 dataset. Our approach significantly improves the speed of data processing. The fast Walsh-Hadamard transform has a computational complexity of O(mlog(2)m). As a result, it is computationally more efficient than the 1 x 1 convolution layer. The fast Walsh-Hadamard layer processes a tensor in R-10x32x32x1024 about 2 times faster than 1 x 1 convolution layer on NVIDIA Jetson Nano computer board.

关键词： Deep learning computer vision Tensors Convolution conferences Transforms computer architecture

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LTNet: Light Transfer Network for Depth Guided Image Relighting

LTNet: Light Transfer Network for Depth Guided Image Relight...

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

作者： Zhu, Yu Ding, Bosong Li, Chenghua Qian, Wanli Li, Fangya Yao, Yiheng Gang, Ruipeng Zhang, Chunjie Cheng, Jian Chinese Acad Sci CASIA Inst Automat Beijing 100190 Peoples R China NRTA Acad Broadcasting Sci Beijing 100866 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China BJTU Beijing Key Lab Adv Informat Sci & Network Techno Beijing Peoples R China CASIA Nanjing Artificial Intelligence Chip Res Nanjing 211100 Peoples R China Georgia Inst Technol Atlanta GA 30318 USA State Key Lab Media Convergence & Commun Beijing 100024 Peoples R China

ISBN: (纸本)9781665448994

Relighting is an interesting yet challenging low-level vision problem, which aims to re-render the scene with new light sources. In this paper, we introduce LTNet, a novel framework for image relighting. Unlike previous methods, we propose to solve this challenging problem by decoupling the enhancement process. Specifically, we propose to train a network that focuses on learning light variations. Our key insight is that light variations are the critical information to be learned because the scene stays unchanged during the light transfer process. To this end, we employ a global residual connection and corresponding residual loss for capturing light variations. Experimental results show that the proposed method achieves better visual quality on the VIDIT dataset in the NTIRE2021 relighting challenge.

关键词： Measurement Visualization computer vision conferences Focusing pattern recognition Task analysis

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EVA-GCN: Head Pose Estimation Based on Graph Convolutional Networks

EVA-GCN: Head Pose Estimation Based on Graph Convolutional N...

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

作者： Xin, Miao Mo, Shentong Lin, Yuanze Chinese Acad Sci Inst Automat CASIA Beijing Peoples R China Carnegie Mellon Univ Pittsburgh PA 15213 USA Beihang Univ Beijing Peoples R China

ISBN: (纸本)9781665448994

Head pose estimation is an important task in many real-world applications. Since the facial landmarks usually serve as the common input that is shared by multiple downstream tasks, utilizing landmarks to acquire high-precision head pose estimation is of practical value for many real-world applications. However, existing landmark-based methods have a major drawback in model expressive power, making them hard to achieve comparable performance to the landmark-free methods. In this paper, we propose a strong baseline method which views the head pose estimation as a graph regression problem. We construct a landmark-connection graph, and propose to leverage the Graph Convolutional Networks (GCN) to model the complex nonlinear mappings between the graph typologies and the head pose angles. Specifically, we design a novel GCN architecture which utilizes joint Edge-Vertex Attention (EVA) mechanism to overcome the unstable landmark detection. Moreover, we introduce the Adaptive Channel Attention (ACA) and the Densely-Connected Architecture (DCA) to boost the performance further. We evaluate the proposed method on three challenging benchmark datasets. Experiment results demonstrate that our method achieves better performance in comparison with the state-of-the-art landmark-based and landmark-free methods.

关键词： computer vision Adaptation models Image edge detection conferences Pose estimation computer architecture Benchmark testing

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ILCOC: An Incremental Learning Framework based on Contrastive One-class Classifiers

ILCOC: An Incremental Learning Framework based on Contrastiv...

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

作者： Sun, Wenju Zhang, Jing Wang, Danyu Geng, Yangli-Ao Li, Qingyong Beijing Jiaotong Univ Beijing Key Lab Traff Data Anal & Min Beijing 100044 Peoples R China

ISBN: (纸本)9781665448994

In the class incremental learning, the number of classes to be handled dynamically raises with the number of considered tasks. The main challenge of this learning schema is catastrophic forgetting, that is the performance degradation on old tasks after learning new tasks. Existing incremental learning algorithms generally choose to train a multi-class classifier (e.g. softmax classifier), which learns a decision boundary to divide the feature space into several parts. Therefore, when new data arrive, the learned boundary will be updated and thus may cause forgetting. Compared with multi-class classifiers, a one-class classifier focuses on characterizing the distribution of a single class. As a result, the decision boundary learned for each category is tighter and does not change during learning new tasks. Inspired by this characteristic of one-class classifier, we propose a novel Incremental Learning framework based on Contrastive One-class Classifiers (ILCOC) to avoid catastrophic forgetting. Specifically, we train a specific one-class classifier for each category and parallelly use them to achieve incremental multi-class recognition. Besides, we design a scale-boundary loss, a classifier-contrastive loss and a negative-suppression loss to strengthen the comparability of classifiers outputs and the discrimination ability of each one-class classifier. We evaluate ILCOC on MNIST, CIFAR-10 and Tiny-ImageNet datasets, and the experimental results show that ILCOC achieves state-of-the-art performance.(1)

关键词： Degradation computer vision Heuristic algorithms conferences Computational modeling pattern recognition Classification algorithms

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Multimodal Controller for Generative Models 1

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International conference on computer vision and Machine Intelligence, CVMI 2022

作者： Diao, Enmao Ding, Jie Tarokh, Vahid Duke University DurhamNC27708 United States University of Minnesota-Twin Cities MinneapolisMN55455 United States

ISBN: (数字)9789811978678

ISBN: (纸本)9789811978661

Class-conditional generative models are crucial tools for data generation from user-specified class labels. Existing approaches for class-conditional generative models require nontrivial modifications of backbone generative architectures to model conditional information fed into the model. This paper introduces a plug-and-play module, named ‘multimodal controller’ to generate multimodal data without introducing additional learning parameters. In the absence of the controllers, our model reduces to non-conditional generative models. We test the efficacy of multimodal controllers on CIFAR10, COIL100, and Omniglot benchmark datasets. We demonstrate that multimodal controlled generative models (including VAE, PixelCNN, Glow, and GAN) can generate class-conditional images of significantly better quality when compared with conditional generative models. Moreover, we show that multimodal controlled models can also create novel modalities of images. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer vision

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computer vision-based Assistance System for the Visually Impaired Using Mobile Edge Artificial Intelligence

Computer Vision-based Assistance System for the Visually Imp...

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

作者： Mahendran, Jagadish K. Barry, Daniel T. Nivedha, Anita K. Bhandarkar, Suchendra M. Kutir Technol Corp Vancouver BC Canada Denbar Robot Boston MA USA Mol Forecaster Inc Montreal PQ Canada Univ Georgia Dept Comp Sci Athens GA 30602 USA

ISBN: (纸本)9781665448994

Despite significant recent developments, visual assistance systems are still severely constrained by sensor capabilities, form factor, battery power consumption, computational resources and the use of traditional computer vision algorithms. Current visual assistance systems cannot adequately perform complex computer vision tasks that entail deep learning. We present the design and implementation of a novel visual assistance system that employs deep learning and point cloud processing to perform advanced perception tasks on a cost-effective, low-power mobile computing platform. The proposed system design circumvents the need for expensive, power-intensive Graphical Processing Unit (GPU)-based hardware required by most deep learning algorithms for real-time inference by employing instead edge Artificial Intelligence (AI) accelerators such as the Neural Compute Stick-2 (NCS2), model optimization techniques such as OpenVINO, and TensorFlow Lite, and smart depth sensors such as OpenCV AI Kit-Depth (OAK-D). Critical system design challenges such as training data collection, real-time capability, computational efficiency, power consumption, portability and reliability are addressed. The proposed system includes more advanced functionality than existing systems such as assessment of traffic conditions and detection and localization of hanging obstacles, crosswalks, moving obstacles and sudden elevation changes. The proposed system design incorporates an AI-based voice interface that allows for user-friendly interaction and control and is shown to realize a simple, cost-effective, power-efficient, portable and unobtrusive visual assistance device.

关键词： Edge Artificial Intelligence (AI) Mobile Computing Visual Assistance Device Deep Learning

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