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共 6545 条记录，以下是261-270 订阅

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CorrGAN: Input Transformation Technique Against Natural Corruptions

CorrGAN: Input Transformation Technique Against Natural Corr...

引用

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

来源：评论

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Unpaired Faces to Cartoons: Improving XGAN

Unpaired Faces to Cartoons: Improving XGAN

引用

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...

引用

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

引用

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...

引用

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...

引用

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...

引用

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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Enriched Robust Multi-View Kernel Subspace Clustering

Enriched Robust Multi-View Kernel Subspace Clustering

引用

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

作者： Zhang, Mengyuan Liu, Kai Clemson Univ Clemson SC 29631 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Subspace clustering is to find underlying low-dimensional subspaces and cluster the data points correctly. In this paper, we propose a novel multi-view subspace clustering method. Most existing methods suffer from two critical issues. First, they usually adopt a two-stage framework and isolate the processes of affinity learning, multi-view information fusion and clustering. Second, they assume the data lies in a linear subspace which may fail in practice as most real-world datasets may have non-linearity structures. To address the above issues, in this paper we propose a novel Enriched Robust Multi-View Kernel Subspace Clustering framework where the consensus affinity matrix is learned from both multi-view data and spectral clustering. Due to the objective and constraints which is difficult to optimize, we propose an iterative optimization method which is easy to implement and can yield closed solution in each step. Extensive experiments have validated the superiority of our method over state-of-the-art clustering methods.

关键词： computer vision Clustering methods conferences Optimization methods Benchmark testing Iterative methods Kernel

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Super-Resolution based Video Coding Scheme

Super-Resolution based Video Coding Scheme

引用

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

作者： Cho, Hyun Min Choi, Kiho Gacheon Univ Sch Comp 1342 Seongnamdaero Seongnam Si Gyeonggi Do South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we present a super-resolution-based video coding scheme that compresses video data by combining traditional hybrid video coding and Convolutional neural network-based video coding. During video encoding, downsampling reduces the resolution of an original video in both horizontal and vertical directions to reduce original video data, and Convolutional neural networkbased super-resolution is employed after the decoding process to recover the resolution of the reconstructed video during upsampling. For core encoding and decoding processes, the latest video coding standard (i.e., VVC/H.266) is conducted. The experimental results show that the proposed method can provide efficient coding performance while maintaining good visual quality.

关键词： Video coding Visualization Image coding Superresolution Encoding Decoding pattern recognition

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Learned Compression of High Dimensional Image Datasets

Learned Compression of High Dimensional Image Datasets

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

作者： Cole, Elizabeth Meng, Qingxi Pauly, John Vasanawala, Shreyas Stanford Univ Dept Elect Engn Stanford CA 94305 USA Stanford Univ Dept Radiol Stanford CA 94305 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In many applications, such as burst photography and magnetic resonance imaging (MRI), multiple images are acquired to reduce the noise of the eventual reconstructed image. However, this leads to very high dimensional datasets which have redundant information across the various acquired images. In MRI, multiple images are acquired via multiple RF coil arrays in the scanner. Afterwards, coil compression is performed to convert the original set of coil images into a smaller set of virtual coil images to enable smaller datasets and faster computation time. However, traditional iterative coil compression methods are lossy and time-consuming. In this work, we propose a novel neural network-based coil compression method in pursuit of higher reconstruction accuracy and faster coil compression. Our learned compression method achieves up to 1.5x lower NRMSE and up to 10 times runtime speed compared to traditional methods on a benchmark test dataset.

关键词： Radio frequency Photography Image coding Runtime Magnetic resonance imaging conferences pattern recognition

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