標(biāo)題: Titlebook: Advances in Visual Computing; 18th International S George Bebis,Golnaz Ghiasi,Luv Kohli Conference proceedings 2023 The Editor(s) (if appli [打印本頁(yè)] 作者: deteriorate 時(shí)間: 2025-3-21 19:55
書目名稱Advances in Visual Computing影響因子(影響力)
書目名稱Advances in Visual Computing影響因子(影響力)學(xué)科排名
書目名稱Advances in Visual Computing網(wǎng)絡(luò)公開(kāi)度
書目名稱Advances in Visual Computing網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書目名稱Advances in Visual Computing被引頻次
書目名稱Advances in Visual Computing被引頻次學(xué)科排名
書目名稱Advances in Visual Computing年度引用
書目名稱Advances in Visual Computing年度引用學(xué)科排名
書目名稱Advances in Visual Computing讀者反饋
書目名稱Advances in Visual Computing讀者反饋學(xué)科排名
作者: VERT 時(shí)間: 2025-3-21 22:59
Deep Learning Based GABA Edited-MRS Signal Reconstructiondata from real GABA-edited ground truths. Our model achieves a 95% decrease in Mean Squared Error (MSE), a 70% decrease in Linewidth, a 450% increase in Signal to Noise Ratio (SNR), and a 42% increase in Peak Shape Score compared to the current existing method on the test set. We also illustrate our作者: 誘惑 時(shí)間: 2025-3-22 03:04 作者: 字形刻痕 時(shí)間: 2025-3-22 07:12
ReFit: A Framework for?Refinement of?Weakly Supervised Semantic Segmentation Using Object Border Fit be used to construct a boundary map, which enables . to predict object locations with sharper boundaries. By applying our method to WSSS predictions, we achieved up to 10% improvement over the current state-of-the-art WSSS methods for medical imaging. The framework is open-source, to ensure that ou作者: 虛弱的神經(jīng) 時(shí)間: 2025-3-22 08:54
A Data-Centric Approach for?Pectoral Muscle Deep Learning Segmentation Enhancements in?Mammography Inhance the accuracy of the deep-learning-based mammography segmentation model. In the first stage, we introduce a shape-based label analysis to automatically identify pectoral muscle labels with possible inconsistencies for a posterior manual review and correction. Then, in the second stage, we down作者: 激勵(lì) 時(shí)間: 2025-3-22 16:14 作者: 合并 時(shí)間: 2025-3-22 18:40 作者: infinite 時(shí)間: 2025-3-22 23:25 作者: 大氣層 時(shí)間: 2025-3-23 03:44 作者: 高射炮 時(shí)間: 2025-3-23 09:37 作者: 漂白 時(shí)間: 2025-3-23 12:29
Pretext Tasks in?Bridge Defect Segmentation Within a?ViT-Adapter Frameworkt (SA-1B) and a small specific dataset. More specifically, SupMAE exhibited a propensity for preparing the segmenter to handle “stuff” defects (Crack, Corrosion, and Spallation), while DINO demonstrated better performance for “thing” defects (Rebar Corrosion).作者: Banister 時(shí)間: 2025-3-23 17:27
https://doi.org/10.1007/978-3-030-57012-5ighting signal. We refer to the proposed loss as Density-based Adaptive Sample-Level Prioritizing (Density-ASP) loss. Our motivation stems from the observation that mass segmentation becomes more challenging as breast density increases. This observation makes density a viable option for controlling 作者: 內(nèi)疚 時(shí)間: 2025-3-23 18:20 作者: 羅盤 時(shí)間: 2025-3-24 00:26
H. F. Chin,P. Quek,U. R. Sinniahle while still focusing on relevant features. We also find that attention improves the correlation between model performance and LayerCAM activation in the region of interest. Our work provides insightful information to help guide the future construction of attention-based models for mammogram class作者: 制定法律 時(shí)間: 2025-3-24 02:54 作者: 放棄 時(shí)間: 2025-3-24 10:26 作者: ASSET 時(shí)間: 2025-3-24 13:22 作者: allergen 時(shí)間: 2025-3-24 18:27
Valery D. Siokhin,Joseph I. Chernychkoareas of volumetric disparity by projecting them onto the face. Our approach substantially minimizes human intervention simplifying the clinical routine and interaction with 3D scans. The proposed pipeline can potentially more effectively analyze and monitor patient treatment progress.作者: 哭得清醒了 時(shí)間: 2025-3-24 19:42 作者: companion 時(shí)間: 2025-3-25 01:07
Conservation Needs and Early Concerns,ve accumulation of the local context information: pose encoding, which encodes the human pose information as an additional feature, and spatial attention, which discriminates the relative context information from the others. Our pipeline accumulates the global and local relation information and gath作者: progestin 時(shí)間: 2025-3-25 06:52
Conservation Needs and Early Concerns,Bootstrapped Language-Image Pre-training based models (BLIP/BLIP-2), which have been shown to be effective for various downstream vision-language tasks, even in zero-shot settings. We show that such models can be easily repurposed as effective, off-the-shelf feature extractors for VMR. On the QVHigh作者: 配置 時(shí)間: 2025-3-25 10:11
Shyamal Dutta,Soumen Chatterjeet (SA-1B) and a small specific dataset. More specifically, SupMAE exhibited a propensity for preparing the segmenter to handle “stuff” defects (Crack, Corrosion, and Spallation), while DINO demonstrated better performance for “thing” defects (Rebar Corrosion).作者: CORD 時(shí)間: 2025-3-25 12:56
0302-9743 neration for Computer Vision?and Robotics in Precision Agriculture..Part 2:?Virtual Reality;?Segmentation;?Applications;?Object Detection and Recognition;?Deep Learning;?Poster.. . . . . . .978-3-031-47968-7978-3-031-47969-4Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Dedication 時(shí)間: 2025-3-25 16:33 作者: 停止償付 時(shí)間: 2025-3-25 20:43
Visualizing Multimodal Time Series at?Scalege volumes of time series and their aggregates in near real time, with a simple yet powerful interface. The visualization synchronized across modalities can provide still further capability for us to develop and verify our hypothesis in multimodal data analysis.作者: d-limonene 時(shí)間: 2025-3-26 00:47
Achim Unger,Arno P. Schniewind,Wibke Ungeropose a novel method for utilizing the spiral layout for order-preserving visualization in HPC monitoring, called .. To demonstrate the effectiveness and usefulness of ., we present the case studies of the application to a real-world temporal, multivariate HPC dataset.作者: Emasculate 時(shí)間: 2025-3-26 07:52 作者: hauteur 時(shí)間: 2025-3-26 10:43 作者: RACE 時(shí)間: 2025-3-26 15:53 作者: 間諜活動(dòng) 時(shí)間: 2025-3-26 18:13
ArcheryVis: A Tool for?Analyzing and?Visualizing Archery Performance Dataace. We achieve automatic shot detection using a deep neural network, compute scores and relevant statistical measures, and design coordinated multiple views for interactive user exploration. Experimental results demonstrate the effectiveness of ArcheryVis.作者: 滔滔不絕的人 時(shí)間: 2025-3-26 22:49 作者: 捐助 時(shí)間: 2025-3-27 01:56 作者: 樂(lè)意 時(shí)間: 2025-3-27 05:56 作者: 排出 時(shí)間: 2025-3-27 12:51 作者: dithiolethione 時(shí)間: 2025-3-27 16:04
Hybrid Region and?Pixel-Level Adaptive Loss for?Mass Segmentation on?Whole Mammography Imageshes for breast cancer detection, there has been a considerable boost in the performance in the field. The loss function is a core element of any deep learning architecture with a significant influence on its performance. The loss function is particularly important for tasks such as breast mass segme作者: 功多汁水 時(shí)間: 2025-3-27 20:26 作者: 美色花錢 時(shí)間: 2025-3-28 00:24
Investigating the?Impact of?Attention on?Mammogram Classificationding of why attention offers improvements is rather limited. In this paper, we present the first comprehensive comparison of different combinations of baseline models and attention methods at multiple resolutions for whole mammogram image classification of masses and calcifications. Our findings ind作者: Ischemic-Stroke 時(shí)間: 2025-3-28 02:29 作者: cataract 時(shí)間: 2025-3-28 09:59 作者: 熄滅 時(shí)間: 2025-3-28 13:20 作者: Mirage 時(shí)間: 2025-3-28 16:33
Hybrid Tree Visualizations for?Analysis of?Gerrymandering political parties. Understanding the relationships between the multiple dimensions in electoral data is a core goal of gerrymandering analysis. In this paper, we analyze patterns of gerrymandering in election data using a hybrid tree visualization technique that supports both overview and drill-dow作者: Melatonin 時(shí)間: 2025-3-28 20:25 作者: 寄生蟲(chóng) 時(shí)間: 2025-3-28 22:54 作者: anthropologist 時(shí)間: 2025-3-29 04:01
From Faces to?Volumes - Measuring Volumetric Asymmetry in?3D Facial Palsy Scans approaches to evaluate the facial palsy state rely mainly on stills and 2D videos of the face and rarely on 3D information. Many of these analysis and visualization methods require manual intervention, which is time-consuming and error-prone. Moreover, existing approaches depend on alignment algori作者: 群居男女 時(shí)間: 2025-3-29 09:48 作者: Frenetic 時(shí)間: 2025-3-29 13:42
Local and?Global Context Reasoning for?Spatio-Temporal Action Localizationthe relationships between the actor and another actor, as well as between the actor and the environment. However, reasoning the relationships globally over the image is not always the efficient way, and there are cases that locally searching for the relative clues is more suitable. In this paper, we作者: 小步走路 時(shí)間: 2025-3-29 18:47 作者: NAV 時(shí)間: 2025-3-29 20:30
Self-supervised Representation Learning for?Fine Grained Human Hand Action Recognition in?Industrialthe objects to be observed, it is equally important to understand the fine-grained hand movements of a human to be able to track the entire process. However, these deep learning based hand action recognition methods are very label intensive, which cannot be offered by all industrial companies due to作者: BARK 時(shí)間: 2025-3-30 01:52 作者: Euthyroid 時(shí)間: 2025-3-30 06:09 作者: antidote 時(shí)間: 2025-3-30 08:32 作者: Hyperlipidemia 時(shí)間: 2025-3-30 12:30
Advances in Visual Computing978-3-031-47969-4Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Irritate 時(shí)間: 2025-3-30 17:53 作者: JIBE 時(shí)間: 2025-3-30 21:50
Seed Banks for Future Generationmical composition and metabolic processes of body tissues. Edited MRS Reconstruction converts raw MRS data into meaningful spectrum signals, providing valuable insights into cellular metabolism, organ function, and energy production. This process can help understand normal physiology, diagnose disea作者: 稀釋前 時(shí)間: 2025-3-31 02:12 作者: RALES 時(shí)間: 2025-3-31 08:44 作者: jungle 時(shí)間: 2025-3-31 12:29
