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检索条件"任意字段=2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006"

共 6849 条记录，以下是261-270 订阅

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An Effective Temporal Localization Method with Multi-View 3D Action recognition for Untrimmed Naturalistic Driving Videos

An Effective Temporal Localization Method with Multi-View 3D...

引用

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

作者： Manh Tung Tran Minh Quan Vu Ngoc Duong Hoang Khac-Hoai Nam Bui Viettel Grp Viettel Cyperspace Ctr Hanoi Vietnam

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Naturalistic driving studies with computer vision techniques have become an emergent research issue. The objective is to classify the distracted behavior actions by drivers. Specifically, this issue is regarded as temporal action localization (TAL) of untrimmed videos, which is a challenging task in the research field of video analysis. Particularly, TAL remains as one of the most challenging unsolved problems in computer vision that requires not only the recognition of action but the localization of the start and end times of each action. Most state-of-the-art approaches adopt complex architectures, which are expensive training and inefficient inference time. In this study, we propose a new framework for untrimmed naturalistic driving videos by utilizing the results from 3D action recognition with video clip classification for short temporal and spatial correlation. Then, simple post-processing based on data-driven is presented for long temporal correlation in untrimmed videos. The proposed method is evaluated on the AI City Challenge 2022 dataset for Naturalistic Driving Action recognition. Accordingly, our method achieves the top 1 on the public leaderboard of the challenge.

关键词： Location awareness Training computer vision Solid modeling Correlation Urban areas X3D

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The Role of Shape for Domain Generalization on Sparsely-Textured Images

The Role of Shape for Domain Generalization on Sparsely-Text...

引用

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

作者： Nazari, Narges Honarvar Kovashka, Adriana Univ Pittsburgh Pittsburgh PA 15260 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

State-of-the-art object recognition methods do not generalize well to unseen domains. Work in domain generalization has attempted to bridge domains by increasing feature compatibility, but has focused on standard, appearance-based representations. We show the potential of shape-based representations to increase domain robustness. We compare two types of shape-based representations: one trains a convolutional network over edge features, and another computes a soft, dense medial axis transform. We show the complementary strengths of these representations for different types of domains, and the effect of the amount of texture that is preserved. We show that our shape-based techniques better leverage data augmentations for domain generalization, and are more effective at texture bias mitigation than shape-inducing augmentations. Finally, we show that when the convolutional network in state-of-the-art domain generalization methods is replaced with one that explicitly captures shape, we obtain improved results.

关键词： Bridges computer vision Shape conferences Transforms Robustness pattern recognition

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CorrGAN: Input Transformation Technique Against Natural Corruptions

CorrGAN: Input Transformation Technique Against Natural Corr...

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

作者： Haque, Mirazul Budnik, Christof J. Yang, Wei UT Dallas Richardson TX 75080 USA Siemens Corp Technol Princeton NJ USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Because of the increasing accuracy of Deep Neural Networks (DNNs) on different tasks, a lot of real times systems are utilizing DNNs. These DNNs are vulnerable to adversarial perturbations and corruptions. Specifically, natural corruptions like fog, blur, contrast etc can affect the prediction of DNN in an autonomous vehicle. In real time, these corruptions are needed to be detected and also the corrupted inputs are needed to be de-noised to be predicted correctly. In this work, we propose CorrGAN approach, which can generate benign input when a corrupted input is provided. In this framework, we train Generative Adversarial Network (GAN) with novel intermediate output-based loss function. The GAN can denoise the corrupted input and generate benign input. Through experimentation, we show that up to 75.2% of the corrupted misclassified inputs can be classified correctly by DNN using CorrGAN.

关键词： Deep learning computer vision Perturbation methods conferences Neural networks computer architecture Generative adversarial networks

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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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2023 ieee/CVF conference on computer vision and pattern recognition Workshops, cvprW 2023

作者： Kollias, Dimitrios Tzirakis, Panagiotis Baird, Alice Cowen, Alan Zafeiriou, Stefanos Queen Mary University of London United Kingdom Hume AI United States Imperial College London United Kingdom

ISBN: (纸本)9798350302493

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. © 2023 ieee.

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Unpaired Faces to Cartoons: Improving XGAN

Unpaired Faces to Cartoons: Improving XGAN

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

作者： Ramos, Stev H. Cabrera, Joel Ibanez, Daniel Jimenez-Panta, Alejandro B. Beltran-Castanon, Cesar Villanueva, Edwin Pontifical Catholic Univ Peru Lima Peru

ISBN: (纸本)9781665487399

Domain Adaptation is a task that aims to translate an image from a source domain to a desired target domain. Current methods in domain adaptation use adversarial training based on Generative Adversarial Networks (GAN). In the present work, we focus on the task of domain adaptation from real faces to cartoon face images. We start from a baseline architecture called XGAN and introduce some improvements to it. Our proposed model is called W-XDGAN, which uses a form of GAN called Wasserstein-GAN, learns to approximate the Wasserstein Distance, and adds a denoiser to smooth the output cartoons. Whereas the original XGAN paper only presented a qualitative analysis, the advantages of this solution are demonstrated both quantitatively and qualitatively by comparing the results with models such as UNIT and original XGAN. Our code and models are publicly available at https://github. com/IAmigos/avatar-image-generator.

关键词： Training computer vision Codes Computational modeling conferences computer architecture Generative adversarial networks

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Continuous Emotion recognition using Visual-audio-linguistic Information: A Technical Report for ABAW3

Continuous Emotion Recognition using Visual-audio-linguistic...

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

作者： Zhang, Su An, Ruyi Ding, Yi Guan, Cuntai Nanyang Technol Univ Singapore Singapore

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We propose a cross-modal co-attention model for continuous emotion recognition using visual-audio-linguistic information. The model consists of four blocks. The visual, audio, and linguistic blocks are used to learn the spatial-temporal features of the multi-modal input. A co-attention block is designed to fuse the learned features with the multi-head co-attention mechanism. The visual encoding from the visual block is concatenated with the attention feature to emphasize the visual information. To make full use of the data and alleviate over-fitting, cross-validation is carried out on the training and validation set. The concordance correlation coefficient (CCC) centering is used to merge the results from each fold. The achieved CCC on the test set is 0.520 for valence and 0.602 for arousal, which significantly outperforms the baseline method with the corresponding CCC of 0.180 and 0.170 for valence and arousal, respectively.

关键词： Training Correlation coefficient Visualization Emotion recognition Fuses Databases Writing

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CoRe: Color Regression for Multicolor Fashion Garments

CoRe: Color Regression for Multicolor Fashion Garments

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

作者： Rame, Alexandre Douillard, Arthur Ollion, Charles Sorbonne Univ Paris France Heuritech Paris France

ISBN: (纸本)9781665487399

Developing deep networks that analyze fashion garments has many real-world applications. Among all fashion attributes, color is one of the most important yet challenging to detect. Existing approaches are classification-based and thus cannot go beyond the list of discrete predefined color names. In this paper, we handle color detection as a regression problem to predict the exact RGB values. That's why in addition to a first color classifier, we include a second regression stage for refinement in our newly proposed architecture. This second step combines two attention models: the first depends on the type of clothing, the second depends on the color previously detected by the classifier. Our final prediction is the weighted spatial pooling over the image pixels RGB values, where the illumination has been corrected. This architecture is modular and easily expanded to detect the RGBs of all colors in a multicolor garment. In our experiments, we show the benefits of each component of our architecture.

关键词： Visualization computer vision Image color analysis conferences Clothing Lighting computer architecture

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Gated Recurrent Unit-Based RNN for Remote Photoplethysmography Signal Segmentation

Gated Recurrent Unit-Based RNN for Remote Photoplethysmograp...

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

作者： Sabour, Rita Meziati Benezeth, Yannick Univ Bourgogne Franche Comte Dijon France Univ Bourgogne Franche Comte ImViA Lab Dijon France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Remote Photoplethysmography (rPPG) enables quantifying blood volume variations in the skin tissues from an input video recording, using a regular RGB camera. Obtained pulse signals often contain noisy portions due to motion, leading researchers to put aside a great number of rPPG signals in their studies. In this paper, an approach using a Gated Recurrent Unit-based neural network model in order to identify reliable portions in rPPG signals is proposed. This is done by classifying rPPG signal samples into reliable and unreliable samples. For this purpose, rPPG and electrocardiography signals (ECG) were collected from 11 participants, rPPG signal samples were labeled (ECG was used as ground truth), and data were augmented to reach a total number of 11000 1-minute-long rPPG signals. We developed a model composed of a unidimensional CNN and a Bidirectional GRU (1D-CNN+B-GRU) for this study, and obtained an accuracy rate of 85.88%.

关键词： computer network reliability Neural networks Electrocardiography Logic gates Photoplethysmography Skin pattern recognition

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Transformer-based Multimodal Information Fusion for Facial Expression Analysis

Transformer-based Multimodal Information Fusion for Facial E...

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

作者： Zhang, Wei Qiu, Feng Wang, Suzhen Zeng, Hao Zhang, Zhimeng An, Rudong Ma, Bowen Ding, Yu Netease Fuxi AI Lab Virtual Human Grp Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Human affective behavior analysis has received much attention in human-computer interaction (HCI). In this paper, we introduce our submission to the cvpr 2022 Competition on Affective Behavior Analysis in-the-wild (ABAW). To fully exploit affective knowledge from multiple views, we utilize the multimodal features of spoken words, speech prosody, and facial expression, which are extracted from the video clips in the Aff-Wild2 dataset. Based on these features, we propose a unified transformer-based multimodal framework for Action Unit detection and also expression recognition. Specifically, the static vision feature is first encoded from the current frame image. At the same time, we clip its adjacent frames by a sliding window and extract three kinds of multimodal features from the sequence of images, audio, and text. Then, we introduce a transformer-based fusion module that integrates the static vision features and the dynamic multimodal features. The cross-attention module in the fusion module makes the output integrated features focus on the crucial parts that facilitate the downstream detection tasks. We also leverage some data balancing techniques, data augmentation techniques, and postprocessing methods to further improve the model performance. In the official test of ABAW3 Competition, our model ranks first in the EXPR and AU tracks. The extensive quantitative evaluations, as well as ablation studies on the Aff-Wild2 dataset, prove the effectiveness of our proposed method.

关键词： Human computer interaction Gold Face recognition conferences Feature extraction Transformers Data models

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Detecting Real-Time Deep-Fake Videos Using Active Illumination

Detecting Real-Time Deep-Fake Videos Using Active Illuminati...

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

作者： Gerstner, Candice R. Farid, Hany Natl Secur Agcy Ft George G Meade MD 20755 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

While many have grown suspicious of viral images and videos found online, there is a general sense that we can and should trust that the person on the other end of our video-conferencing call is who it purports to be. The real-time creation of sophisticated deep fakes, however, is making it more difficult to trust even live video calls. Detecting deep fakes in real time introduces new challenges as compared to off-line forensic analyses. We describe a technique for detecting, in real-time, deep-fake videos transmitted over a live video-conferencing application. This technique leverages the fact that a video call typically places a user in front of a light source (the computer display) which can be manipulated to induce a controlled change in the appearance of the user's face. Deviations of the expected change in appearance over time can be measured in real time and used to verify the authenticity of a video-call participant.

关键词： Deepfakes Teleconferencing Forensics Face recognition Lighting Particle measurements Real-time systems

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