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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"

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Deflating Dataset Bias Using Synthetic Data Augmentation

Deflating Dataset Bias Using Synthetic Data Augmentation

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Nikita Jaipuria Xianling Zhang Rohan Bhasin Mayar Arafa Punarjay Chakravarty Shubham Shrivastava Sagar Manglani Vidya N. Murali Ford Greenfield Labs Palo Alto

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Deep Learning has seen an unprecedented increase in vision applications since the publication of large-scale object recognition datasets and introduction of scalable compute hardware. State-of-the-art methods for most vision tasks for Autonomous Vehicles (AVs) rely on supervised learning and often fail to generalize to domain shifts and/or outliers. Dataset diversity is thus key to successful real-world deployment. No matter how big the size of the dataset, capturing long tails of the distribution pertaining to task-specific environmental factors is impractical. The goal of this paper is to investigate the use of targeted synthetic data augmentation - combining the benefits of gaming engine simulations and sim2real style transfer techniques - for filling gaps in real datasets for vision tasks. Empirical studies on three different computer vision tasks of practical use to AVs -parking slot detection, lane detection and monocular depth estimation - consistently show that having synthetic data in the training mix provides a significant boost in cross-dataset generalization performance as compared to training on real data only, for the same size of the training set.

关键词： Task analysis Data models Training computer vision Estimation Gallium nitride Autonomous vehicles

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The 1st Challenge on Remote Physiological Signal Sensing (RePSS)

The 1st Challenge on Remote Physiological Signal Sensing (Re...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Xiaobai Li Hu Han Hao Lu Xuesong Niu Zitong Yu Antitza Dantcheva Guoying Zhao Shiguang Shan Center for Machine Vision and Signal Analysis University of Oulu Finland Key Laboratory of Intelligient Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology China STARS team INRIA France

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Remote measurement of physiological signals from videos is an emerging topic. The topic draws great interests, but the lack of publicly available benchmark databases and a fair validation platform are hindering its further development. For this concern, we organize the first challenge on Remote Physiological Signal Sensing (RePSS), in which two databases of VIPL and OBF are provided as the benchmark for kin researchers to evaluate their approaches. The 1st challenge of RePSS focuses on measuring the average heart rate from facial videos, which is the basic problem of remote physiological measurement. This paper presents an overview of the challenge, including data, protocol, analysis of results and discussion. The top ranked solutions are highlighted to provide insights for researchers, and future directions are outlined for this topic and this challenge.

关键词： Videos Physiology Heart rate Testing Databases Cameras Training

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Improving the affordability of robustness training for DNNs

Improving the affordability of robustness training for DNNs

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Sidharth Gupta Parijat Dube Ashish Verma University of Illinois Urbana-Champaign IBM Research

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Projected Gradient Descent (PGD) based adversarial training has become one of the most prominent methods for building robust deep neural network models. However, the computational complexity associated with this approach, due to the maximization of the loss function when finding adversaries, is a longstanding problem and may be prohibitive when using larger and more complex models. In this paper we show that the initial phase of adversarial training is redundant and can be replaced with natural training which significantly improves the computational efficiency. We demonstrate that this efficiency gain can be achieved without any loss in accuracy on natural and adversarial test samples. We support our argument with insights on the nature of the adversaries and their relative strength during the training process. We show that our proposed method can reduce the training time by a factor of up to 2.5 with comparable or better model test accuracy and generalization on various strengths of adversarial attacks.

关键词： Training Mathematical model Computational modeling Robustness Neural networks computer architecture Optimization

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Upright and Stabilized Omnidirectional Depth Estimation for Wide-baseline Multi-camera Inertial Systems

Upright and Stabilized Omnidirectional Depth Estimation for ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Changhee Won Hochang Seok Jongwoo Lim MultiplEYE Co. Ltd. Seoul Korea Department of Computer Science Hanyang University Seoul Korea

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

This paper presents an upright and stabilized omnidirectional depth estimation for an arbitrarily rotated wide- baseline multi-camera inertial system. By aligning the reference rig coordinate system with the gravity direction acquired from an inertial measurement unit, we sample depth hypotheses for omnidirectional stereo matching by sweeping global spheres whose equators are parallel to the ground plane. Then, unary features extracted from each input image by 2D convolutional neural networks (CNN) are warped onto the swept spheres, and the final omnidirectional depth map is output through cost computation by a 3D CNN-based hourglass module and a softargmax operation. This can eliminate wavy or unrecognizable visual artifacts in equirectangular depth maps which can cause failures in scene understanding. We show the capability of our upright and stabilized omnidirectional depth estimation through experiments on real data.

关键词： Cameras Estimation Gravity Feature extraction Three-dimensional displays Visualization Neural networks

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Detecting Deepfake Videos using Attribution-Based Confidence Metric

Detecting Deepfake Videos using Attribution-Based Confidence...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Steven Fernandes Sunny Raj Rickard Ewetz Jodh Singh Pannu Sumit Kumar Jha Eddy Ortiz Iustina Vintila Margaret Salter Department of Computer Science Electrical and Computer Engineering University of Central Orlando Florida FL Solution Acceleration and Innovation Department Royal Bank of Canada

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Recent advances in generative adversarial networks have made detecting fake videos a challenging task. In this paper, we propose the application of the state-of-the-art attribution based confidence (ABC) metric for detecting deepfake videos. The ABC metric does not require access to the training data or training the calibration model on the validation data. The ABC metric can be used to draw inferences even when only the trained model is available. Here, we utilize the ABC metric to characterize whether a video is original or fake. The deep learning model is trained only on original videos. The ABC metric uses the trained model to generate confidence values. For, original videos, the confidence values are greater than 0.94.

关键词： Videos Face Measurement Computational modeling Training data Machine learning Databases

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Learning A Meta-Ensemble Technique For Skin Lesion Classification And Novel Class Detection

Learning A Meta-Ensemble Technique For Skin Lesion Classific...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Subhranil Bagchi Anurag Banerjee Deepti R. Bathula Department of Computer Science and Engineering Indian Institute of Technology Ropar India

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

The frequency and fatality rates associated with skin Melanoma requires an accurate and efficient detection methodology to enable early medical diagnosis. Artificial Intelligence (AI) augmented detection methods aim at achieving this goal while reducing the costs and time involved in traditional methods. This work utilizes a two-level ensemble learning technique (trained with weighted losses) to improve accuracy over individual classification models. The ensemble technique alleviates over-fitting due to class imbalance in the dataset, achieving a Balanced Multi-class Accuracy (BMA) score of 0.591 without unknown class detection. The algorithm was extended by appending the proposed CS-KSU module collection to detect the presence of images belonging to novel classes during test time. The extended algorithm secured an Area Under the ROC Curve (AUC) score of 0.544 for the unknown class. Our algorithm's performance is at par with the current state-of-the-art for this task 1 .

关键词： Stacking Training Skin Task analysis Lesions Image color analysis Melanoma

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Reducing catastrophic forgetting with learning on synthetic data

Reducing catastrophic forgetting with learning on synthetic ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Wojciech Masarczyk Ivona Tautkute Institute of Theoretical and Applied Informatics Polish Academy of Sciences Polish-Japanese Academy of Information Technology

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Catastrophic forgetting is a problem caused by neural networks' inability to learn data in sequence. After learning two tasks in sequence, performance on the first one drops significantly. This is a serious disadvantage that prevents many deep learning applications to real-life problems where not all object classes are known beforehand; or change in data requires adjustments to the model. To reduce this problem we investigate the use of synthetic data, namely we answer a question: Is it possible to generate such data synthetically which learned in sequence does not result in catastrophic forgetting? We propose a method to generate such data in two-step optimisation process via meta-gradients. Our experimental results on Split-MNIST dataset show that training a model on such synthetic data in sequence does not result in catastrophic forgetting. We also show that our method of generating data is robust to different learning scenarios.

关键词： Task analysis Optimization Generators Data models Neural networks Training computer architecture

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Moiré pattern Removal via Attentive Fractal Network

Moiré Pattern Removal via Attentive Fractal Network

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Dejia Xu Yihao Chu Qingyan Sun Peking University Beijing University of Posts and Telecommunications Beijing Jiaotong University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Moiré patterns are commonly seen artifacts when taking photos of screens and other objects with high-frequency textures. It's challenging to remove the moiré patterns considering its complex color and shape. In this work, we propose an Attentive Fractal Network to effectively solve this problem. First, we construct each Attentive Fractal Block with progressive feature fusion and channel-wise attention guidance. The network is then fractally stacked with the block on each of its levels. Second, to further boost the performance, we adopt a two-stage augmented refinement strategy. With these designs, our method wins the burst demoiréing track and achieves second place in single image demoireing and single image deblurring tracks inNTIRE20 Challenges. Extensive experiments demonstrate the superiority of our method for moiré pattern removal compared to existing state-of-the-art methods, and prove the effectiveness of its each component. We will publicly release our code and trained weights on https://***/ir1d/AFN.

关键词： Fractals Image restoration Image color analysis Shape Machine learning Task analysis Cameras

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Yoga-82: A New Dataset for Fine-grained Classification of Human Poses

Yoga-82: A New Dataset for Fine-grained Classification of Hu...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Manisha Verma Sudhakar Kumawat Yuta Nakashima Shanmuganathan Raman Osaka University Japan Indian Institute of Technology Gandhinagar India

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Human pose estimation is a well-known problem in computer vision to locate joint positions. Existing datasets for learning of poses are observed to be not challenging enough in terms of pose diversity, object occlusion and view points. This makes the pose annotation process relatively simple and restricts the application of the models that have been trained on them. To handle more variety in human poses, we propose the concept of fine-grained hierarchical pose classification, in which we formulate the pose estimation as a classification task, and propose a dataset, Yoga-82, for large-scale yoga pose recognition with 82 classes. Yoga-82 consists of complex poses where fine annotations may not be possible. To resolve this, we provide hierarchical labels for yoga poses based on the body configuration of the pose. The dataset contains a three-level hierarchy including body positions, variations in body positions, and the actual pose names. We present the classification accuracy of the state-of-the-art convolutional neural network architectures on Yoga-82. We also present several hierarchical variants of DenseNet in order to utilize the hierarchical labels.

关键词： Legged locomotion Wheels Pose estimation computer vision Visualization Skeleton Image resolution

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Implicit Euler ODE Networks for Single-Image Dehazing

Implicit Euler ODE Networks for Single-Image Dehazing

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jiawei Shen Zhuoyan Li Lei Yu Gui-Song Xia Wen Yang School of Electronic and Information Wuhan University School of Computer Science Wuhan University Wuhan China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Deep convolutional neural networks (CNN) have been applied for image dehazing tasks, where the residual network (ResNet) is often adopted as the basic component to avoid the vanishing gradient problem. Recently, many works indicate that the ResNet can be considered as the explicit Euler forward approximation of an ordinary differential equation (ODE). In this paper, we extend the explicit forward approximation to the implicit backward counterpart, which can be realized via a recursive neural network, named IM-block. Given that, we propose an efficient end-to-end multi-level implicit network (MI-Net) for the single image dehazing problem. Moreover, multi-level fusing (MLF) mechanism and residual channel attention block (RCA-block) are adopted to boost performance of our network. Experiments on several dehazing benchmark datasets demonstrate that our method outperforms existing methods and achieves the state-of-the-art performance.

关键词： Atmospheric modeling Task analysis Convolution Neural networks Image color analysis Visualization Scattering

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