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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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computer-vision Based Attention Monitoring for Online Meetings 5

Computer-Vision Based Attention Monitoring for Online Meetin...

引用

5th International conference on pattern recognition and Artificial Intelligence, PRAI 2022

作者： Dacayan, Tristram Kwak, Daehan Zhang, Xudong Kean University School of Computer Science and Technology UnionNJ United States

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

Due to the appearance of COVID-19, virtual video conferencing platforms like Zoom and Google Meet have become one of the main alternative ways to conduct virtual meetings and presentations. While the virtual platforms are cheaper and more flexible, presenters and meeting hosts are likely less efficient at assessing audience attention and engagement due to the lack of body language. In this paper, we propose a system for estimating and monitoring participant attention in virtual meetings by using computer vision. Our approach mainly focuses on changes in a person's presence, gaze direction, and head orientation as a computer camera has a limited field of view. We first created a module to detect and extract participant video cells to isolate users and process their attention individually. Using those videos, we then monitored the user's presence, using YOLOv3 and DeepSORT, and their gaze direction and head orientation, using PTGaze. Through this monitoring, the system is able to record and graph a user's attention over the total amount of frames and return a collective attention level graph for the entire meeting. We believe that our system has potential usage in settings where attention is critical, such as academic lectures or collaborative business meetings. © 2022 IEEE.

关键词： computer vision

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Cloud2Curve: Generation and Vectorization of Parametric Sketches

Cloud2Curve: Generation and Vectorization of Parametric Sket...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Das, Ayan Yang, Yongxin Hospedales, Timothy Xiang, Tao Song, Yi-Zhe Univ Surrey CVSSP SketchX Guildford Surrey England iFlyTek Surrey Joint Res Ctr Artificial Intellige Guildford Surrey England Univ Edinburgh Edinburgh Midlothian Scotland

ISBN: (纸本)9781665445092

Analysis of human sketches in deep learning has advanced immensely through the use of waypoint-sequences rather than raster-graphic representations. We further aim to model sketches as a sequence of low-dimensional parametric curves. To this end, we propose an inverse graphics framework capable of approximating a raster or waypoint based stroke encoded as a point-cloud with a variable-degree Bezier curve. Building on this module, we present Cloud2Curve, a generative model for scalable high-resolution vector sketches that can be trained end-to-end using point-cloud data alone. As a consequence, our model is also capable of deterministic vectorization which can map novel raster or waypoint based sketches to their corresponding high-resolution scalable Bezier equivalent. We evaluate the generation and vectorization capabilities of our model on Quick, Draw! and K-MNIST datasets.

关键词： Training Graphics Deep learning computer vision Computational modeling Fitting computer architecture

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Consistent Instance False Positive Improves Fairness in Face recognition

Consistent Instance False Positive Improves Fairness in Face...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Xu, Xingkun Huang, Yuge Shen, Pengcheng Li, Shaoxin Li, Jilin Huang, Feiyue Li, Yong Cui, Zhen Tencent YoutuLab Shenzhen Peoples R China Nanjing Univ Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781665445092

Demographic bias is a significant challenge in practical face recognition systems. Existing methods heavily rely on accurate demographic annotations. However, such annotations are usually unavailable in real scenarios. Moreover, these methods are typically designed for a specific demographic group and are not general enough. In this paper, we propose a false positive rate penalty loss, which mitigates face recognition bias by increasing the consistency of instance False Positive Rate (FPR). Specifically, we first define the instance FPR as the ratio between the number of the non-target similarities above a unified threshold and the total number of the non-target similarities. The unified threshold is estimated for a given total FPR. Then, an additional penalty term, which is in proportion to the ratio of instance FPR overall FPR, is introduced into the denominator of the softmax-based loss. The larger the instance FPR, the larger the penalty. By such unequal penalties, the instance FPRs are supposed to be consistent. Compared with the previous debiasing methods, our method requires no demographic annotations. Thus, it can mitigate the bias among demographic groups divided by various attributes, and these attributes are not needed to be previously predefined during training. Extensive experimental results on popular benchmarks demonstrate the superiority of our method over state-of-the-art competitors.

关键词： Training computer vision Codes Annotations Face recognition Benchmark testing

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Unsupervised Detection of Cancerous Regions in Histology Imagery using Image-to-Image Translation

Unsupervised Detection of Cancerous Regions in Histology Ima...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Stepec, Dejan Skocaj, Danijel Univ Ljubljana Fac Comp & Informat Sci Vecna Pot 113 Ljubljana 1000 Slovenia XLAB Doo Pot Za Brdom 100 Ljubljana 1000 Slovenia

ISBN: (纸本)9781665448994

Detection of visual anomalies refers to the problem of finding patterns in different imaging data that do not conform to the expected visual appearance and is a widely studied problem in different domains. Due to the nature of anomaly occurrences and underlying generating processes, it is hard to characterize them and obtain labeled data. Obtaining labeled data is especially difficult in biomedical applications, where only trained domain experts can provide labels, which often come in large diversity and complexity. Recently presented approaches for unsupervised detection of visual anomalies approaches omit the need for labeled data and demonstrate promising results in domains, where anomalous samples significantly deviate from the normal appearance. Despite promising results, the performance of such approaches still lags behind supervised approaches and does not provide a one-fits-all solution. In this work, we present an image-to-image translation-based framework that significantly surpasses the performance of existing unsupervised methods and approaches the performance of supervised methods in a challenging domain of cancerous region detection in histology imagery.

关键词： Visualization computer vision Image analysis Histopathology conferences Biomedical measurement pattern recognition

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FlowStep3D: Model Unrolling for Self-Supervised Scene Flow Estimation

FlowStep3D: Model Unrolling for Self-Supervised Scene Flow E...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Kittenplon, Yair Eldar, Yonina C. Raviv, Dan Tel Aviv Univ Tel Aviv Israel Weizmann Inst Sci Rehovot Israel

ISBN: (纸本)9781665445092

Estimating the 3D motion of points in a scene, known as scene flow, is a core problem in computer vision. Traditional learning-based methods designed to learn end-to-end 3D flow often suffer from poor generalization. Here we present a recurrent architecture that learns a single step of an unrolled iterative alignment procedure for refining scene flow predictions. Inspired by classical algorithms, we demonstrate iterative convergence toward the solution using strong regularization. The proposed method can handle sizeable temporal deformations and suggests a slimmer architecture than competitive all-to-all correlation approaches. Trained on FlyingThings3D synthetic data only, our network successfully generalizes to real scans, outperforming all existing methods by a large margin on the KITTI self-supervised benchmark.(1)

关键词： Training Learning systems computer vision Three-dimensional displays Refining Estimation computer architecture

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Intentonomy: a Dataset and Study towards Human Intent Understanding

Intentonomy: a Dataset and Study towards Human Intent Unders...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Jia, Menglin Wu, Zuxuan Reiter, Austin Cardie, Claire Belongie, Serge Lim, Ser-Nam Cornell Univ Ithaca NY 14853 USA Facebook AI Menlo Pk CA 94025 USA Fudan Univ Shanghai Peoples R China

ISBN: (纸本)9781665445092

An image is worth a thousand words, conveying information that goes beyond the mere visual content therein. In this paper, we study the intent behind social media images with an aim to analyze how visual information can facilitate recognition of human intent. Towards this goal, we introduce an intent dataset, Intentonomy, comprising 14K images covering a wide range of everyday scenes. These images are manually annotated with 28 intent categories derived from a social psychology taxonomy. We then systematically study whether, and to what extent, commonly used visual information, i.e., object and context, contribute to human motive understanding. Based on our findings, we conduct further study to quantify the effect of attending to object and context classes as well as textual information in the form of hashtags when training an intent classifier. Our results quantitatively and qualitatively shed light on how visual and textual information can produce observable effects when predicting intent.(1)

关键词： Training Visualization computer vision Image recognition Social networking (online) Taxonomy Psychology

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Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision

Semi-Supervised Semantic Segmentation with Cross Pseudo Supe...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Chen, Xiaokang Yuan, Yuhui Zeng, Gang Wang, Jingdong Peking Univ Key Lab Machine Percept MOE Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China Microsoft Res Beijing Peoples R China

ISBN: (纸本)9781665445092

In this paper, we study the semi-supervised semantic segmentation problem via exploring both labeled data and extra unlabeled data. We propose a novel consistency regularization approach, called cross pseudo supervision (CPS). Our approach imposes the consistency on two segmentation networks perturbed with different initialization for the same input image. The pseudo one-hot label map, output from one perturbed segmentation network, is used to supervise the other segmentation network with the standard cross-entropy loss, and vice versa. The CPS consistency has two roles: encourage high similarity between the predictions of two perturbed networks for the same input image, and expand training data by using the unlabeled data with pseudo labels. Experiment results show that our approach achieves the state-of-the-art semi-supervised segmentation performance on Cityscapes and PASCAL VOC 2012.

关键词： Training Image segmentation computer vision Semantics Training data pattern recognition Standards

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DER: Dynamically Expandable Representation for Class Incremental Learning

DER: Dynamically Expandable Representation for Class Increme...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Yan, Shipeng Xie, Jiangwei He, Xuming ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Shanghai Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781665445092

We address the problem of class incremental learning, which is a core step towards achieving adaptive vision intelligence. In particular, we consider the task setting of incremental learning with limited memory and aim to achieve better stability-plasticity trade-off. To this end, we propose a novel two-stage learning approach that utilizes a dynamically expandable representation for more effective incremental concept modeling. Specifically, at each incremental step, we freeze the previously learned representation and augment it with additional feature dimensions from a new learnable feature extractor. This enables us to integrate new visual concepts with retaining learned knowledge. We dynamically expand the representation according to the complexity of novel concepts by introducing a channel-level mask-based pruning strategy. Moreover, we introduce an auxiliary loss to encourage the model to learn diverse and discriminate features for novel concepts. We conduct extensive experiments on the three class incremental learning benchmarks and our method consistently outperforms other methods with a large margin.(1)

关键词： Visualization computer vision Adaptation models Benchmark testing Feature extraction pattern recognition Complexity theory

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The Blessings of Unlabeled Background in Untrimmed Videos

The Blessings of Unlabeled Background in Untrimmed Videos

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Liu, Yuan Chen, Jingyuan Chen, Zhenfang Deng, Bing Huang, Jianqiang Zhang, Hanwang Alibaba Grp Hangzhou Peoples R China Univ Hong Kong Hong Kong Peoples R China Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9781665445092

Weakly-supervised Temporal Action Localization (WTAL) aims to detect the action segments with only video-level action labels in training. The key challenge is how to distinguish the action of interest segments from the background, which is unlabelled even on the video-level. While previous works treat the background as "curses", we consider it as "blessings". Specifically, we first use causal analysis to point out that the common localization errors are due to the unobserved confounder that resides ubiquitously in visual recognition. Then, we propose a Temporal Smoothing PCA-based (TS-PCA) deconfounder, which exploits the unlabelled background to model an observed substitute for the unobserved confounder, to remove the confounding effect. Note that the proposed deconfounder is model-agnostic and non-intrusive, and hence can be applied in any WTAL method without model re-designs. Through extensive experiments on four state-of-the-art WTAL methods, we show that the deconfounder can improve all of them on the public datasets: THUMOS-14 and ActivityNet-1.3(1).

关键词： Location awareness Training Visualization computer vision Smoothing methods Computational modeling pattern recognition

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BalaGAN: Cross-Modal Image Translation Between Imbalanced Domains

BalaGAN: Cross-Modal Image Translation Between Imbalanced Do...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Patashnik, Or Danon, Dov Zhang, Hao Cohen-Or, Daniel Tel Aviv Univ Tel Aviv Israel Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781665448994

State-of-the-art image translation methods tend to struggle in an imbalanced domain setting, where one image domain lacks richness and diversity. We introduce a new unsupervised translation network, BalaGAN, specifically designed to tackle the domain imbalance problem. We leverage the latent modalities of the richer domain to turn the image-to-image translation problem, between two imbalanced domains, into a multi-class translation problem, more resembling the style transfer setting. Specifically, we analyze the source domain and learn a decomposition of it into a set of latent modes or classes, without any supervision. This leaves us with a multitude of balanced crossdomain translation tasks, between all pairs of classes, including the target domain. During inference, the trained network takes as input a source image, as well as a reference style image from one of the modes as a condition, and produces an image which resembles the source on the pixel-wise level, but shares the same mode as the reference. We show that employing modalities within the dataset improves the quality of the translated images, and that BalaGAN outperforms strong baselines of both unconditioned and style-transfer-based image-to-image translation methods, in terms of image quality and diversity.

关键词： Training Image quality computer vision conferences pattern recognition Task analysis

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