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检索条件"任意字段=IEEE Information Visualization Conference/IEEE Visualization Conference"

共 54122 条记录，以下是4711-4720 订阅

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Attention-based multiple instance learning with self-supervision to predict microsatellite instability in colorectal cancer from histology whole-slide images 44

Attention-based multiple instance learning with self-supervi...

引用

44th Annual International conference of the ieee-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Leiby, Jacob S. Hao, Jie Kang, Gyeong Hoon Park, Ji Won Kim, Dokyoon Univ Penn Perelman Sch Med Dept Biostat Epidemiol & Informat Philadelphia PA 19104 USA Univ Penn Philadelphia PA 19104 USA Chinese Acad Med Sci & Peking Union Med Coll Inst Med Informat Beijing Peoples R China Seoul Natl Univ Seoul Natl Univ Hosp Dept Pathol Coll Med Seoul South Korea Seoul Natl Univ Canc Res Inst Seoul South Korea Seoul Natl Univ Coll Med Seoul Natl Univ Hosp Dept Surg Seoul South Korea Univ Penn Inst Biomed Informat Philadelphia PA 19104 USA

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Microsatellite instability (MSI) is a clinically important characteristic of colorectal cancer. Standard diagnosis of MSI is performed via genetic analyses, however these tests are not always included in routine care. Histopathology whole-slide images (WSIs) are the gold-standard for colorectal cancer diagnosis and are routinely collected. This study develops a model to predict MSI directly from WSIs. Making use of both weakly- and self-supervised deep learning techniques, the proposed model shows improved performance over conventional deep learning models. Additionally, the proposed framework allows for visual interpretation of model decisions. These results are validated in internal and external testing datasets.

关键词： Deep learning visualization Histopathology Biological system modeling Small satellites Predictive models Genetics

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Virtual Correspondence: Humans as a Cue for Extreme-View Geometry

Virtual Correspondence: Humans as a Cue for Extreme-View Geo...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ma, Wei-Chiu Yang, Anqi Joyce Wang, Shenlong Urtasun, Raquel Torralba, Antonio MIT 77 Massachusetts Ave Cambridge MA 02139 USA Univ Toronto Toronto ON Canada Univ Illinois Urbana IL USA Waabi Toronto ON Canada Vector Inst Toronto ON Canada

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recovering the spatial layout of the cameras and the geometry of the scene from extreme-view images is a long-standing challenge in computer vision. Prevailing 3D reconstruction algorithms often adopt the image matching paradigm and presume that a portion of the scene is co-visible across images, yielding poor performance when there is little overlap among inputs. In contrast, humans can associate visible parts in one image to the corresponding invisible components in another image via prior knowledge of the shapes. Inspired by this fact, we present a novel concept called virtual correspondences (VCs). VCs are a pair of pixels from two images whose camera rays intersect in 3D. Similar to classic correspondences, VCs conform with epipolar geometry;unlike classic correspondences, VCs do not need to be co-visible across views. Therefore VCs can be established and exploited even if images do not overlap. We introduce a method to find virtual correspondences based on humans in the scene. We showcase how VCs can be seamlessly integrated with classic bundle adjustment to recover camera poses across extreme views. Experiments show that our method significantly outperforms state-of-the-art camera pose estimation methods in challenging scenarios and is comparable in the traditional densely captured setup. Our approach also unleashes the potential of multiple downstream tasks such as scene reconstruction from multi-view stereo and novel view synthesis in extreme-view scenarios(1).

关键词： Geometry Computer vision visualization Shape Pose estimation Semantics Layout

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Toward Fast, Flexible, and Robust Low-Light Image Enhancement

Toward Fast, Flexible, and Robust Low-Light Image Enhancemen...

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ma, Long Ma, Tengyu Liu, Risheng Fan, Xin Luo, Zhongxuan Dalian Univ Technol Sch Software Technol Dalian Peoples R China Dalian Univ Technol DUT RU Int Sch Informat Sci & Engn Dalian Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Existing low-light image enhancement techniques are mostly not only difficult to deal with both visual quality and computational efficiency but also commonly invalid in unknown complex scenarios. In this paper, we develop a new Self-Calibrated Illumination (SCI) learning framework for fast, flexible, and robust brightening images in real-world low-light scenarios. To be specific, we establish a cascaded illumination learning process with weight sharing to handle this task. Considering the computational burden of the cascaded pattern, we construct the self-calibrated module which realizes the convergence between results of each stage, producing the gains that only use the single basic block for inference (yet has not been exploited in previous works), which drastically diminishes computation cost. We then define the unsupervised training loss to elevate the model capability that can adapt general scenes. Further, we make comprehensive explorations to excavate SCI's inherent properties (lacking in existing works) including operation-insensitive adaptability (acquiring stable performance under the settings of different simple operations) and model-irrelevant generality (can be applied to illumination-based existing works to improve performance). Finally, plenty of experiments and ablation studies fully indicate our superiority in both quality and efficiency. Applications on low-light face detection and nighttime semantic segmentation fully reveal the latent practical values for SCI. The source code is available at https://***/vis-opt-group/SCI.

关键词： Training Adaptation models visualization Image segmentation Computational modeling Semantics Lighting

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Retrieval-based Spatially Adaptive Normalization for Semantic Image Synthesis

Retrieval-based Spatially Adaptive Normalization for Semanti...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Shi, Yupeng Liu, Xiao Wei, Yuxiang Wu, Zhongqin Zuo, Wangmeng Tomorrow Adv Life Beijing Peoples R China Harbin Inst Technol Harbin Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Semantic image synthesis is a challenging task with many practical applications. Albeit remarkable progress has been made in semantic image synthesis with spatially-adaptive normalization, existing methods usually normalize the feature activations under the coarse-level guidance (e.g., semantic class). However, different parts of a semantic object (e.g., wheel and window of car) are quite different in structures and textures, making blurry synthesis results usually inevitable due to the missing of fine-grained guidance. In this paper, we propose a novel normalization module, termed as REtrieval-based Spatially Adaptive normaLization (RESAIL), for introducing pixel level fine-grained guidance to the normalization architecture. Specifically, we first present a retrieval paradigm by finding a content patch of the same semantic class from training set with the most similar shape to each test semantic mask. Then, the retrieved patches are composited into retrieval-based guidance, which can be used by RESAIL for pixel level fine-grained modulation on feature activations, thereby greatly mitigating blurry synthesis results. Moreover, distorted ground-truth images are also utilized as alternatives of retrieval-based guidance for feature normalization, further benefiting model training and improving visual quality of generated images. Experiments on several challenging datasets show that our RESAIL performs favorably against state-of-the-arts in terms of quantitative metrics, visual quality, and subjective evaluation.

关键词： Training visualization Image synthesis Shape Navigation Semantics Wheels

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Expressive Talking Head Generation with Granular Audio-Visual Control

Expressive Talking Head Generation with Granular Audio-Visua...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Liang, Borong Pan, Yan Guo, Zhizhi Zhou, Hang Hong, Zhibin Han, Xiaoguang Han, Junyu Liu, Jingtuo Ding, Errui Wang, Jingdong Baidu Inc Dept Comp Vis Technol Vis Beijing Peoples R China CUHK Shenzhen SSE Shenzhen Peoples R China CUHK Shenzhen FNii Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Generating expressive talking heads is essential for creating virtual humans. However, existing one- or few-shot methods focus on lip-sync and head motion, ignoring the emotional expressions that make talking faces realistic. In this paper, we propose the Granularly Controlled Audio-Visual Talking Heads (GC-AVT), which controls lip movements, head poses, and facial expressions of a talking head in a granular manner. Our insight is to decouple the audio-visual driving sources through prior-based pre-processing designs. Detailedly, we disassemble the driving image into three complementary parts including: 1) a cropped mouth that facilitates lip-sync;2) a masked head that implicitly learns pose;and 3) the upper face which works corporately and complementarily with a time-shifted mouth to contribute the expression. Interestingly, the encoded features from the three sources are integrally balanced through reconstruction training. Extensive experiments show that our method generates expressive faces with not only synced mouth shapes, controllable poses, but precisely animated emotional expressions as well.

关键词： Training visualization Shape Lips Pipelines Mouth Speech recognition

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The Design & Evaluation of an information visualization to Improve the Efficiency & Understanding of Patient Health State

The Design & Evaluation of an Information Visualization to I...

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ieee International conference on Healthcare Informatics (ICHI)

作者： Ahmed Mohammed Patel Dyson School of Design Engineering Imperial College London London UK

Family physicians face the need to process and interpret an ever-increasing amount of patient data, which challenges their ability to address all relevant information in order to make informed decisions that influence patients’ lives. Healthcare professionals report frustration in locating, customising and prioritising data in the Electronic Health Record, with almost no time before or during the consultation to review the patient’s information. Presenting heterogeneous sources of data requires effective visualizations with considered organisation so users are not ‘overwhelmed’, and to guide the understanding and interpretation of information. This study seeks to design and evaluate a holistic information visualization, to provide clinicians in Primary Care with a quick overview and ‘essence’ of the patient. This offers an opportunity to capture and communicate a longitudinal history and story of a patient alongside assisting clinicians in coping with information overload, enhancing situation awareness, and efficiency in the understanding of the presented *** first stage of the postgraduate study investigates visualizations in healthcare: its role, importance, applications, benefits and limitations through a scoping literature review. The second stage manifests the design of an information visualization, developed iteratively whilst applying both a bottom-up and a top-down approach. This includes understanding EHR users and other stakeholders’ needs and wants via interviews, focus groups and *** eliciting opportunities in enhancing physicians’ situation awareness and reducing their cognitive overload, we conducted interviews with eight General Practitioners to explore what important information is required before and during the GP-patient consultation to enhance situation awareness, when is the right time to display this information, what is the desired format and what are the main barriers to gaining situation awareness [1]. Patient-information tha

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The Value of Visual Attention for COVID-19 Classification in CT Scans 18

The Value of Visual Attention for COVID-19 Classification in...

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18th ieee/CVF International conference on Computer Vision (ICCV)

作者： Rao, Adrit Park, Jongchan Aalami, Oliver Palo Alto High Sch Palo Alto CA 94301 USA Lunit Inc Seoul South Korea Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781665401913

Detecting COVID-19 in early stages is crucial in order to initiate timely treatment of disease. COVID-19 screening with chest CT scans has been utilized due to the rapidity of results and robustness. Computer vision aided medical diagnosis with deep learning models can improve accuracy and efficiency of screening. When developing models for high-risk medical classification tasks, it is important to aim to reach radiologist level interpretation in terms of cognition. When the human brain analyzes visual information, cognitive visual attention is applied in order to apply more focus onto higher frequency regions of interest. Using attention mechanisms in order to infer channel and spatial attention maps within convolutional neural networks can improve the performance in classification of COVID-19 changes. Through performing a compact study with a quantitative accuracy measure along with a qualitative visualization of activation heat-maps, we study the benefits of visual self-attention for the classification of COVID-19.

关键词： COVID-19 Heating systems Deep learning visualization Computed tomography conferences Convolutional neural networks

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SEGA: Semantic Guided Attention on Visual Prototype for Few-Shot Learning 22

SEGA: Semantic Guided Attention on Visual Prototype for Few-...

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22nd ieee/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Yang, Fengyuan Wang, Ruiping Chen, Xilin Chinese Acad Sci Key Lab Intelligent Informat Proc Inst Comp Technol Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Beijing Acad Artificial Intelligence Beijing 100084 Peoples R China

ISBN: (纸本)9781665409155

Teaching machines to recognize a new category based on few training samples especially only one remains challenging owing to the incomprehensive understanding of the novel category caused by the lack of data. However, human can learn new classes quickly even given few samples since human can tell what discriminative features should be focused on about each category based on both the visual and semantic prior knowledge. To better utilize those prior knowledge, we propose the SEmantic Guided Attention (SEGA) mechanism where the semantic knowledge is used to guide the visual perception in a top-down manner about what visual features should be paid attention to when distinguishing a category from the others. As a result, the embedding of the novel class even with few samples can be more discriminative. Concretely, a feature extractor is trained to embed few images of each novel class into a visual prototype with the help of transferring visual prior knowledge from base classes. Then we learn a network that maps semantic knowledge to category-specific attention vectors which will be used to perform feature selection to enhance the visual prototypes. Extensive experiments on minilmageNet, tieredImageNet, CIFAR-FS, and CUB indicate that our semantic guided attention realizes anticipated function and outperforms state-of-the-art results.

关键词： Training Knowledge engineering visualization Computer vision Correlation Semantics Prototypes

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Real-time Tweets Analysis using Machine Learning and Bigdata

Real-time Tweets Analysis using Machine Learning and Bigdata

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North Karnataka Subsection Flagship International conference (NKCon), ieee

作者： P Nandieswar Reddy Sai Aswath S Rithvika Alapati Radha D Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350364569

ISBN: (纸本)9798350364576

Twitter's exponential increase in data has transformed it into a rich source for machine learning research, unveiling patterns of opinions and behaviors. This research introduces an automated pipeline built on Kafka, adept at handling large volumes of real-time Twitter data. Utilizing the Twitter API, tweets bearing the hashtag #justice are retrieved and efficiently processed via Kafka. PySpark facilitates data analysis by enabling aggregation, Jaccard similarity computation, and k-means clustering. Preprocessing with Python libraries and TF-IDF enhances analysis accuracy. Jaccard similarity aids in comparing tweets across socio-economic categories. Aggregated data is stored in MongoDB for further examination. Additionally, Streamlit offers visualization tools for effortless exploration of Twitter discourse trends. Thus, this project exemplifies the potential of big data and its impact on investigating social justice discourse in the digital age.

关键词： Social networking (online) Blogs Pipelines Data visualization Machine learning Market research information age Real-time systems Libraries Socioeconomics

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Physical Interactive Localization Learning

Physical Interactive Localization Learning

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ieee International conference on Advanced Robotics and Its Social Impacts (ARSO)

作者： Haris, Muhammad Franzius, Mathias Bauer-Wersing, Ute Frankfurt Univ Appl Sci Fac Comp Sci & Engn D-60318 Frankfurt Germany Honda Res Inst Europe GmbH D-63073 Offenbach Germany

ISBN: (纸本)9781665479660

Localization is fundamental for mobile robots, especially in unconstrained outdoor environments. Earlier work showed unsupervised localization learning on landmarks to be suitable for large-scale scenes. However, this relied on hand-labeled data to train a CNN for recognizing landmarks. We propose a new approach that allows a robot to learn landmarks for localization with a human cooperatively. This approach uses pre-trained detectors of common objects for learning new landmarks in a scene, requiring only minimal human supervision. Hence, the method bootstraps the landmark learning process and removes the need to manually label large amounts of data. We present localization results using the learned landmarks in simulated and real-world outdoor environments and compare the results to models based on complete images and PoseNet. The landmark-based localization shows improved performance than the baseline methods in challenging scenarios. Our results further show that localization accuracy increases with the number of learned landmarks. The human teacher has complete control over selecting new landmarks, which allows learning unique, robustly detectable, and semantic landmarks.

关键词： Location awareness Training Representation learning visualization Vacuum systems Service robots Semantics

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