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共 8962 条记录，以下是411-420 订阅

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Perfusion assessment via local remote photoplethysmography (rPPG)

Perfusion assessment via local remote photoplethysmography (...

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

作者： Kossack, Benjamin Wisotzky, Eric Eisert, Peter Schraven, Sebastian P. Globke, Brigitta Hilsmann, Anna Fraunhofer Heinrich Hertz Inst Berlin Germany Humboldt Univ Berlin Germany Rostock Univ Dept Otorhinolaryngol Head & Neck Surg Otto Korne Med Ctr Rostock Germany Charite Univ Med Berlin Dept Surg Berlin Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper presents an approach to assess the perfusion of visible human tissue from RGB video files. We propose metrics derived from remote photoplethysmography (rPPG) signals to detect whether a tissue is adequately supplied with blood. The perfusion analysis is done in three different scales, offering a flexible approach for different applications. We perform a plane-orthogonal-to-skin rPPG independently for locally defined regions of interest on each scale. From the extracted signals, we derive the signal-to-noise ratio, magnitude in the frequency domain, heart rate, perfusion index as well as correlation between specific rPPG signals in order to locally assess the perfusion of a specific region of human tissue. We show that locally resolved rPPG has a broad range of applications. As exemplary applications, we present results in intraoperative perfusion analysis and visualization during skin and organ transplantation as well as an application for liveliness assessment for the detection of presentation attacks to authentication systems.

关键词： Visualization Surgery Authentication Photoplethysmography Skin pattern recognition Monitoring

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Deep density estimation based on multi-spectral remote sensing data for in-field crop yield forecasting

Deep density estimation based on multi-spectral remote sensi...

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

作者： Baghdasaryan, Liana Melikbekyan, Razmik Dolmajain, Arthur Hobbs, Jennifer Intelinair Inc Yerevan Armenia Intelinair Inc Chicago IL USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Yield forecasting has been a central task in computational agriculture because of its impact on agricultural management from the individual farmer to the government level. With advances in remote sensing technology, computational processing power, and machine learning, the ability to forecast yield has improved substantially over the past years. However, most previous work has been done leveraging low-resolution satellite imagery and forecasting yield at the region, county, or occasionally farm-level. In this work, we use high-resolution aerial imagery and output from high-precision harvesters to predict in-field harvest values for corn-raising farms in the US Midwest. By using the harvester information, we are able to cast the problem of yield-forecasting as a density estimation problem and predict a harvest rate, in bushels/acre, at every pixel in the field image. This approach provides the farmer with a detailed view of which areas of the farm may be performing poorly so he can make the appropriate management decisions in addition to providing an improved prediction of total yield. We evaluate both traditional machine learning approaches with hand-crafted features alongside deep learning methods. We demonstrate the superiority of our pixel-level approach based on an encoder-decoder framework which produces a 5.41% MAPE at the field-level.

关键词： Deep learning Satellites conferences Government Estimation Crops pattern recognition

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DeSI: Deepfake Source Identifier for Social Media

DeSI: Deepfake Source Identifier for Social Media

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

作者： Narayan, Kartik Agarwal, Harsh Mittal, Surbhi Thakral, Kartik Kundu, Suman Vatsa, Mayank Singh, Richa IIT Jodhpur Karwar Rajasthan India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Social media holds the power to influence a significant change in the population. Through social media, people all around the world can connect and share their views. However, this social space is now infected due to the infiltration of fraudulent, obscene, fake and possibly, influential media. According to a UNESCO report, prevalence of fake news and deepfake content possess the potential of spreading fake propaganda and can lead to political and social unrest. Trust on social media is an emerging problem and there is an urgent need to address the same. There has been some research around approaches that detect fake news and deepfakes, however, identification of the source of these deepfakes posted on social media platforms is an equally important but relatively unexplored challenge. This paper proposes a novel Deepfake Source Identification (DeSI) algorithm that identifies the sources of deepfakes posted on Twitter. The proposed DeSI algorithm allows for two input modalities - text and images. We rigorously test our algorithm in both constrained and unconstrained experimental setups and report the observed results. In the constrained setting, the algorithm correctly identifies all the deepfake tweets as well their sources. The complete framework is further encased in a web portal to facilitate intuitive use and analysis of the results.

关键词： Deepfakes computer vision Social networking (online) Scalability conferences Blogs Sociology

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CENet: Consolidation-and-Exploration Network for Continuous Domain Adaptation

CENet: Consolidation-and-Exploration Network for Continuous ...

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

作者： Zhang, Chi Cheng, Yalu Wei, Pengxu He, Hongliang Chen, Jie Peking Univ Sch Elect & Comp Engn Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Unsupervised Domain Adaptation (UDA) deals with transferring knowledge from labeled source domains to an unlabeled target domain under domain shift. However, this does not reflect the breadth of scenarios that arise in real-world applications since source domains could increase. A plausible conjecture is: can we train a life-long learning model learned on continuous source domains given the target without the presence of labels? We formalize this task as the Continuous Domain Adaptation (CDA) and empirically show that conventional domain adaptation methods may suffer severe generalization deterioration due to the limited incremental transferability and negative transfer. To tackle this issue, we propose a novel sample-to-sample framework-Consolidationand-Exploration Network (CENet) to facilitate incremental transferring. This method underscores both the qualitative and quantitative relationship between samples. Moreover, we conduct comprehensive experiments to evaluate the effectiveness of each component in our pair-based method. Extensive experiments show that our approach achieves significant improvement over related state-of-the-art methods.

关键词： Representation learning Adaptation models computer vision Computational modeling conferences Reliability engineering Data models

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Multi stain graph fusion for multimodal integration in pathology

Multi stain graph fusion for multimodal integration in patho...

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

作者： Dwivedi, Chaitanya Nofallah, Shima Pouryahya, Maryam Iyer, Janani Leidal, Kenneth Chung, Chuhan Watkins, Timothy Billin, Andrew Myers, Robert Abel, John Behrooz, Ali PathAI Inc Boston MA 02215 USA Gilead Sci Inc Foster City CA 94404 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In pathology, tissue samples are assessed using multiple staining techniques to enhance contrast in unique histologic features. In this paper, we introduce a multimodal CNN-GNN based graph fusion approach that leverages complementary information from multiple non-registered histopathology images to predict pathologic scores. We demonstrate this approach in nonalcoholic steatohepatitis (NASH) by predicting CRN fibrosis stage and NAFLD Activity Score (NAS). Primary assessment of NASH typically requires liver biopsy evaluation on two histological stains: Trichrome (TC) and hematoxylin and eosin (H&E). Our multimodal approach learns to extract complementary information from TC and H&E graphs corresponding to each stain while simultaneously learning an optimal policy to combine this information. We report up to 20% improvement in predicting fibrosis stage and NAS component grades over single-stain modeling approaches, measured by computing linearly weighted Cohen's kappa between machinederived vs. pathologist consensus scores. Broadly, this paper demonstrates the value of leveraging diverse pathology images for improved ML-powered histologic assessment.

关键词： Weight measurement computer vision Histopathology Computational modeling conferences Liver Predictive models

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Unsupervised Continual Learning for Gradually Varying Domains

Unsupervised Continual Learning for Gradually Varying Domain...

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

作者： Taufique, Abu Md Niamul Jahan, Chowdhury Sadman Savakis, Andreas Rochester Inst Technol Rochester NY 14623 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In Unsupervised Domain Adaptation (UDA), a network is trained on a source domain and adapted on a target domain where no labeled data is available. Existing UDA techniques consider having the entire target domain available at once, which may not be feasible during deployment in realistic settings where batches of target data are acquired over time. Continual Learning (CL) has been dealing with data constrained paradigms in a supervised manner, where batches of labeled samples are sequentially presented to the network and the network continually learns from the new data without forgetting what was previously learned. Our method for unsupervised continual learning serves as a bridge between the UDA and CL paradigms. This research addresses a gradually evolving target domain fragmented into multiple sequential batches where the model continually adapts to the gradually varying stream of data in an unsupervised manner. To tackle this challenge, we propose a source free method based on episodic memory replay with buffer management. A contrastive loss is incorporated for better alignment of the buffer samples and the continual stream of batches. Our experiments on the rotating MNIST and CORe50 datasets confirm the benefits of our unsupervised continual learning method for gradually varying domains.

关键词： Learning systems Bridges computer vision Adaptation models Codes conferences Memory management

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BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix

BCI: Breast Cancer Immunohistochemical Image Generation thro...

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

作者： Liu, Shengjie Zhu, Chuang Xu, Feng Jia, Xinyu Shi, Zhongyue Jin, Mulan Beijing Univ Posts & Telecommun Beijing Peoples R China Capital Med Univ Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The evaluation of human epidermal growth factor receptor 2 (HER2) expression is essential to formulate a precise treatment for breast cancer. The routine evaluation of HER2 is conducted with immunohistochemical techniques (IHC), which is very expensive. Therefore, for the first time, we propose a breast cancer immunohistochemical (BCI) benchmark attempting to synthesize IHC data directly with the paired hematoxylin and eosin (HE) stained images. The dataset contains 4870 registered image pairs, covering a variety of HER2 expression levels. Based on BCI, as a minor contribution, we further build a pyramid pix2pix image generation method, which achieves better HE to IHC translation results than the other current popular algorithms. Extensive experiments demonstrate that BCI poses new challenges to the existing image translation research. Besides, BCI also opens the door for future pathology studies in HER2 expression evaluation based on the synthesized IHC images. BCI dataset can be downloaded from https://***/BCI.

关键词： Pathology computer vision Image synthesis conferences Breast tissue Epidermis Breast cancer

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Towards Open-Set Object Detection and Discovery

Towards Open-Set Object Detection and Discovery

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

作者： Zheng, Jiyang Li, Weihao Hong, Jie Petersson, Lars Barnes, Nick Australian Natl Univ Canberra ACT Australia Data61 CSIRO Sydney NSW Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

With the human pursuit of knowledge, open-set object detection (OSOD) has been designed to identify unknown objects in a dynamic world. However, an issue with the current setting is that all the predicted unknown objects share the same category as "unknown", which require incremental learning via a human-in-the-loop approach to label novel classes. In order to address this problem, we present a new task, namely Open-Set Object Detection and Discovery (OSODD). This new task aims to extend the ability of open-set object detectors to further discover the categories of unknown objects based on their visual appearance without human effort. We propose a two-stage method that first uses an open-set object detector to predict both known and unknown objects. Then, we study the representation of predicted objects in an unsupervised manner and discover new categories from the set of unknown objects. With this method, a detector is able to detect objects belonging to known classes and define novel categories for objects of unknown classes with minimal supervision. We show the performance of our model on the MS-COCO dataset under a thorough evaluation protocol. We hope that our work will promote further research towards a more robust real-world detection system.

关键词： Visualization computer vision Protocols conferences Object detection Detectors Human in the loop

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Contrastive Regularization for Semi-Supervised Learning

Contrastive Regularization for Semi-Supervised Learning

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

作者： Lee, Doyup Kim, Sungwoong Kim, Ildoo Cheon, Yeongjae Cho, Minsu Han, Wook-Shin POSTECH Pohang South Korea Kakao Brain Seongnam South Korea Kakao Enterprise Seongnam South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Consistency regularization on label predictions becomes a fundamental technique in semi-supervised learning, but it still requires a large number of training iterations for high performance. In this study, we analyze that the consistency regularization restricts the propagation of labeling information due to the exclusion of samples with unconfident pseudo-labels in the model updates. Then, we propose contrastive regularization to improve both efficiency and accuracy of the consistency regularization by well-clustered features of unlabeled data. In specific, after strongly augmented samples are assigned to clusters by their pseudolabels, our contrastive regularization updates the model so that the features with confident pseudo-labels aggregate the features in the same cluster, while pushing away features in different clusters. As a result, the information of confident pseudo-labels can be effectively propagated into more unlabeled samples during training by the well-clustered features. On benchmarks of semi-supervised learning tasks, our contrastive regularization improves the previous consistency-based methods and achieves state-of-the-art results, especially with fewer training iterations. Our method also shows robust performance on open-set semi-supervised learning where unlabeled data includes out-of-distribution samples.

关键词： Training computer vision Analytical models conferences Memory management Semisupervised learning Benchmark testing

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OpenSentinelMap: A Large-Scale Land Use Dataset using OpenStreetMap and Sentinel-2 Imagery

OpenSentinelMap: A Large-Scale Land Use Dataset using OpenSt...

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

作者： Johnson, Noah Treible, Wayne Crispell, Daniel Vis Syst Inc Riverside RI 02915 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Remote sensing data is plentiful, but downloading, organizing, and transforming large amounts of data into a format readily usable by modern machine learning methods is a challenging and labor-intensive task. We present the OpenSentinelMap dataset, which consists of 137,045 unique 3.7 km(2) spatial cells, each containing multiple multispectral Sentinel-2 images captured over a 4 year time period and a set of corresponding per-pixel semantic labels derived from OpenStreetMap data. The labels are not necessarily mutually exclusive, and contain information about roads, buildings, water, and 12 land-use categories. The spatial cells are selected randomly on a global scale over areas of human activity, without regard to OpenStreetMap data availability or quality, making the dataset ideal for both supervised, semi-supervised, and unsupervised experimentation. To demonstrate the effectiveness of the dataset, we a) train an off-the-shelf convolutional neural network with minimal modification to predict land-use and building and road location from multispectral Sentinel-2 imagery and b) show that the learned embeddings are useful for downstream fine-grained classification tasks without any fine-tuning. The dataset is publicly available at https://***/open-sentinel-map/.

关键词： Training Annotations Roads Semantics Buildings pattern recognition Task analysis

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