Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition最新文献_第3页

End-to-end globally consistent registration of multiple point clouds 多点云的端到端全局一致注册

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2020-06-14 DOI: 10.3929/ETHZ-B-000458888

Zan Gojcic, Caifa Zhou, J. D. Wegner, L. Guibas, Tolga Birdal

引用次数: 1

Gum-Net: Unsupervised Geometric Matching for Fast and Accurate 3D Subtomogram Image Alignment and Averaging. Gum-Net：用于快速精确三维子图图像对齐和平均的无监督几何匹配。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2020-06-01 Epub Date: 2020-08-05 DOI: 10.1109/cvpr42600.2020.00413

Xiangrui Zeng, Min Xu

{"title":"Gum-Net: Unsupervised Geometric Matching for Fast and Accurate 3D Subtomogram Image Alignment and Averaging.","authors":"Xiangrui Zeng, Min Xu","doi":"10.1109/cvpr42600.2020.00413","DOIUrl":"10.1109/cvpr42600.2020.00413","url":null,"abstract":"We propose a Geometric unsupervised matching Network (Gum-Net) for finding the geometric correspondence between two images with application to 3D subtomogram alignment and averaging. Subtomogram alignment is the most important task in cryo-electron tomography (cryo-ET), a revolutionary 3D imaging technique for visualizing the molecular organization of unperturbed cellular landscapes in single cells. However, subtomogram alignment and averaging are very challenging due to severe imaging limits such as noise and missing wedge effects. We introduce an end-to-end trainable architecture with three novel modules specifically designed for preserving feature spatial information and propagating feature matching information. The training is performed in a fully unsupervised fashion to optimize a matching metric. No ground truth transformation information nor category-level or instance-level matching supervision information is needed. After systematic assessments on six real and nine simulated datasets, we demonstrate that Gum-Net reduced the alignment error by 40 to 50% and improved the averaging resolution by 10%. Gum-Net also achieved 70 to 110 times speedup in practice with GPU acceleration compared to state-of-the-art subtomogram alignment methods. Our work is the first 3D unsupervised geometric matching method for images of strong transformation variation and high noise level. The training code, trained model, and datasets are available in our open-source software AITom.","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"2020 ","pages":"4072-4082"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7955792/pdf/nihms-1675395.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25485821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generating Accurate Pseudo-labels in Semi-Supervised Learning and Avoiding Overconfident Predictions via Hermite Polynomial Activations. 在半监督学习中生成准确的伪标签，通过赫米特多项式激活避免过度自信的预测

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2020-06-01 Epub Date: 2020-08-05 DOI: 10.1109/cvpr42600.2020.01145

Vishnu Suresh Lokhande, Songwong Tasneeyapant, Abhay Venkatesh, Sathya N Ravi, Vikas Singh

{"title":"Generating Accurate Pseudo-labels in Semi-Supervised Learning and Avoiding Overconfident Predictions via Hermite Polynomial Activations.","authors":"Vishnu Suresh Lokhande, Songwong Tasneeyapant, Abhay Venkatesh, Sathya N Ravi, Vikas Singh","doi":"10.1109/cvpr42600.2020.01145","DOIUrl":"10.1109/cvpr42600.2020.01145","url":null,"abstract":"Rectified Linear Units (ReLUs) are among the most widely used activation function in a broad variety of tasks in vision. Recent theoretical results suggest that despite their excellent practical performance, in various cases, a substitution with basis expansions (e.g., polynomials) can yield significant benefits from both the optimization and generalization perspective. Unfortunately, the existing results remain limited to networks with a couple of layers, and the practical viability of these results is not yet known. Motivated by some of these results, we explore the use of Hermite polynomial expansions as a substitute for ReLUs in deep networks. While our experiments with supervised learning do not provide a clear verdict, we find that this strategy offers considerable benefits in semi-supervised learning (SSL) / transductive learning settings. We carefully develop this idea and show how the use of Hermite polynomials based activations can yield improvements in pseudo-label accuracies and sizable financial savings (due to concurrent runtime benefits). Further, we show via theoretical analysis, that the networks (with Hermite activations) offer robustness to noise and other attractive mathematical properties. Code is available on //GitHub.","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"2020 ","pages":"11432-11440"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7811889/pdf/nihms-1656467.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38836139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Discovering Synchronized Subsets of Sequences: A Large Scale Solution. 发现序列的同步子集:一个大规模的解决方案。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2020-06-01 Epub Date: 2020-08-05 DOI: 10.1109/cvpr42600.2020.00951

Evangelos Sariyanidi, Casey J Zampella, Keith G Bartley, John D Herrington, Theodore D Satterthwaite, Robert T Schultz, Birkan Tunc

{"title":"Discovering Synchronized Subsets of Sequences: A Large Scale Solution.","authors":"Evangelos Sariyanidi, Casey J Zampella, Keith G Bartley, John D Herrington, Theodore D Satterthwaite, Robert T Schultz, Birkan Tunc","doi":"10.1109/cvpr42600.2020.00951","DOIUrl":"https://doi.org/10.1109/cvpr42600.2020.00951","url":null,"abstract":"Finding the largest subset of sequences (i.e., time series) that are correlated above a certain threshold, within large datasets, is of significant interest for computer vision and pattern recognition problems across domains, including behavior analysis, computational biology, neuroscience, and finance. Maximal clique algorithms can be used to solve this problem, but they are not scalable. We present an approximate, but highly efficient and scalable, method that represents the search space as a union of sets called ϵ-expanded clusters, one of which is theoretically guaranteed to contain the largest subset of synchronized sequences. The method finds synchronized sets by fitting a Euclidean ball on ϵ-expanded clusters, using Jung's theorem. We validate the method on data from the three distinct domains of facial behavior analysis, finance, and neuroscience, where we respectively discover the synchrony among pixels of face videos, stock market item prices, and dynamic brain connectivity data. Experiments show that our method produces results comparable to, but up to 300 times faster than, maximal clique algorithms, with speed gains increasing exponentially with the number of input sequences.","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"2020 ","pages":"9490-9499"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/cvpr42600.2020.00951","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38412981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Augmenting Colonoscopy using Extended and Directional CycleGAN for Lossy Image Translation. 使用扩展和定向 CycleGAN 增强结肠镜检查的有损图像转换。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2020-06-01 Epub Date: 2020-08-05 DOI: 10.1109/cvpr42600.2020.00475

Shawn Mathew, Saad Nadeem, Sruti Kumari, Arie Kaufman

{"title":"Augmenting Colonoscopy using Extended and Directional CycleGAN for Lossy Image Translation.","authors":"Shawn Mathew, Saad Nadeem, Sruti Kumari, Arie Kaufman","doi":"10.1109/cvpr42600.2020.00475","DOIUrl":"10.1109/cvpr42600.2020.00475","url":null,"abstract":"Colorectal cancer screening modalities, such as optical colonoscopy (OC) and virtual colonoscopy (VC), are critical for diagnosing and ultimately removing polyps (precursors of colon cancer). The non-invasive VC is normally used to inspect a 3D reconstructed colon (from CT scans) for polyps and if found, the OC procedure is performed to physically traverse the colon via endoscope and remove these polyps. In this paper, we present a deep learning framework, Extended and Directional CycleGAN, for lossy unpaired image-to-image translation between OC and VC to augment OC video sequences with scale-consistent depth information from VC, and augment VC with patient-specific textures, color and specular highlights from OC (e.g, for realistic polyp synthesis). Both OC and VC contain structural information, but it is obscured in OC by additional patient-specific texture and specular highlights, hence making the translation from OC to VC lossy. The existing CycleGAN approaches do not handle lossy transformations. To address this shortcoming, we introduce an extended cycle consistency loss, which compares the geometric structures from OC in the VC domain. This loss removes the need for the CycleGAN to embed OC information in the VC domain. To handle a stronger removal of the textures and lighting, a Directional Discriminator is introduced to differentiate the direction of translation (by creating paired information for the discriminator), as opposed to the standard CycleGAN which is direction-agnostic. Combining the extended cycle consistency loss and the Directional Discriminator, we show state-of-the-art results on scale-consistent depth inference for phantom, textured VC and for real polyp and normal colon video sequences. We also present results for realistic pendunculated and flat polyp synthesis from bumps introduced in 3D VC models.","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"2020 ","pages":"4695-4704"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7811175/pdf/nihms-1660601.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38761939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Can Facial Pose and Expression Be Separated with Weak Perspective Camera? 弱透视相机能分离面部姿态和表情吗?

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2020-06-01 Epub Date: 2020-08-05 DOI: 10.1109/cvpr42600.2020.00720

Evangelos Sariyanidi, Casey J Zampella, Robert T Schultz, Birkan Tunc

{"title":"Can Facial Pose and Expression Be Separated with Weak Perspective Camera?","authors":"Evangelos Sariyanidi, Casey J Zampella, Robert T Schultz, Birkan Tunc","doi":"10.1109/cvpr42600.2020.00720","DOIUrl":"https://doi.org/10.1109/cvpr42600.2020.00720","url":null,"abstract":"Separating facial pose and expression within images requires a camera model for 3D-to-2D mapping. The weak perspective (WP) camera has been the most popular choice; it is the default, if not the only option, in state-of-the-art facial analysis methods and software. WP camera is justified by the supposition that its errors are negligible when the subjects are relatively far from the camera, yet this claim has never been tested despite nearly 20 years of research. This paper critically examines the suitability of WP camera for separating facial pose and expression. First, we theoretically show that WP causes pose-expression ambiguity, as it leads to estimation of spurious expressions. Next, we experimentally quantify the magnitude of spurious expressions. Finally, we test whether spurious expressions have detrimental effects on a common facial analysis application, namely Action Unit (AU) detection. Contrary to conventional wisdom, we find that severe pose-expression ambiguity exists even when subjects are not close to the camera, leading to large false positive rates in AU detection. We also demonstrate that the magnitude and characteristics of spurious expressions depend on the point distribution model used to model the expressions. Our results suggest that common assumptions about WP need to be revisited in facial expression modeling, and that facial analysis software should encourage and facilitate the use of the true camera model whenever possible.","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"2020 ","pages":"7171-7180"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/cvpr42600.2020.00720","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38373442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Predicting Goal-directed Human Attention Using Inverse Reinforcement Learning. 利用反强化学习预测目标导向的人类注意力

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2020-06-01 Epub Date: 2020-08-05 DOI: 10.1109/cvpr42600.2020.00027

Zhibo Yang, Lihan Huang, Yupei Chen, Zijun Wei, Seoyoung Ahn, Gregory Zelinsky, Dimitris Samaras, Minh Hoai

{"title":"Predicting Goal-directed Human Attention Using Inverse Reinforcement Learning.","authors":"Zhibo Yang, Lihan Huang, Yupei Chen, Zijun Wei, Seoyoung Ahn, Gregory Zelinsky, Dimitris Samaras, Minh Hoai","doi":"10.1109/cvpr42600.2020.00027","DOIUrl":"10.1109/cvpr42600.2020.00027","url":null,"abstract":"Human gaze behavior prediction is important for behavioral vision and for computer vision applications. Most models mainly focus on predicting free-viewing behavior using saliency maps, but do not generalize to goal-directed behavior, such as when a person searches for a visual target object. We propose the first inverse reinforcement learning (IRL) model to learn the internal reward function and policy used by humans during visual search. We modeled the viewer's internal belief states as dynamic contextual belief maps of object locations. These maps were learned and then used to predict behavioral scanpaths for multiple target categories. To train and evaluate our IRL model we created COCO-Search18, which is now the largest dataset of high-quality search fixations in existence. COCO-Search18 has 10 participants searching for each of 18 target-object categories in 6202 images, making about 300,000 goal-directed fixations. When trained and evaluated on COCO-Search18, the IRL model outperformed baseline models in predicting search fixation scanpaths, both in terms of similarity to human search behavior and search efficiency. Finally, reward maps recovered by the IRL model reveal distinctive target-dependent patterns of object prioritization, which we interpret as a learned object context.","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"2020 ","pages":"190-199"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8218821/pdf/nihms-1715372.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39020515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Putting visual object recognition in context. 将视觉对象识别置于上下文中。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2020-06-01 Epub Date: 2020-08-05 DOI: 10.1109/CVPR42600.2020.01300

Mengmi Zhang, Claire Tseng, Gabriel Kreiman

{"title":"Putting visual object recognition in context.","authors":"Mengmi Zhang, Claire Tseng, Gabriel Kreiman","doi":"10.1109/CVPR42600.2020.01300","DOIUrl":"https://doi.org/10.1109/CVPR42600.2020.01300","url":null,"abstract":"Context plays an important role in visual recognition. Recent studies have shown that visual recognition networks can be fooled by placing objects in inconsistent contexts (e.g. a cow in the ocean). To understand and model the role of contextual information in visual recognition, we systematically and quantitatively investigated ten critical properties of where, when, and how context modulates recognition including amount of context, context and object resolution, geometrical structure of context, context congruence, time required to incorporate contextual information, and temporal dynamics of contextual modulation. The tasks involve recognizing a target object surrounded with context in a natural image. As an essential benchmark, we first describe a series of psychophysics experiments, where we alter one aspect of context at a time, and quantify human recognition accuracy. To computationally assess performance on the same tasks, we propose a biologically inspired context aware object recognition model consisting of a two-stream architecture. The model processes visual information at the fovea and periphery in parallel, dynamically incorporates both object and contextual information, and sequentially reasons about the class label for the target object. Across a wide range of behavioral tasks, the model approximates human level performance without retraining for each task, captures the dependence of context enhancement on image properties, and provides initial steps towards integrating scene and object information for visual recognition.","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"2020 ","pages":"12982-12991"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/CVPR42600.2020.01300","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39452945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 36

Indian Plant Recognition in the Wild 印度野生植物识别

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2019-12-22 DOI: 10.1007/978-981-15-8697-2_41

Vamsidhar Muthireddy, C. V. Jawahar

引用次数: 0

Metric Learning for Image Registration. 图像注册的度量学习

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2019-06-01 Epub Date: 2020-01-09 DOI: 10.1109/cvpr.2019.00866

Marc Niethammer, Roland Kwitt, François-Xavier Vialard

{"title":"Metric Learning for Image Registration.","authors":"Marc Niethammer, Roland Kwitt, François-Xavier Vialard","doi":"10.1109/cvpr.2019.00866","DOIUrl":"10.1109/cvpr.2019.00866","url":null,"abstract":"Image registration is a key technique in medical image analysis to estimate deformations between image pairs. A good deformation model is important for high-quality estimates. However, most existing approaches use ad-hoc deformation models chosen for mathematical convenience rather than to capture observed data variation. Recent deep learning approaches learn deformation models directly from data. However, they provide limited control over the spatial regularity of transformations. Instead of learning the entire registration approach, we learn a spatially-adaptive regularizer within a registration model. This allows controlling the desired level of regularity and preserving structural properties of a registration model. For example, diffeomorphic transformations can be attained. Our approach is a radical departure from existing deep learning approaches to image registration by embedding a deep learning model in an optimization-based registration algorithm to parameterize and data-adapt the registration model itself. Source code is publicly-available at https://github.com/uncbiag/registration.","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"2019 ","pages":"8455-8464"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7286567/pdf/nihms-1033311.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38036233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0