標(biāo)題: Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2021; 30th International C Igor Farka?,Paolo Masulli,Stefan Wermter Conference proc [打印本頁] 作者: formation 時間: 2025-3-21 18:50
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書目名稱Artificial Neural Networks and Machine Learning – ICANN 2021讀者反饋
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2021讀者反饋學(xué)科排名
作者: 條約 時間: 2025-3-21 23:08
DRENet: Giving Full Scope to Detection and Regression-Based Estimation for Video Crowd Countingheir comparable results, most of these counting methods disregard the fact that crowd density varies enormously in the spatial and temporal domains of videos. This thus hinders the improvement in performance of video crowd counting. To overcome that issue, a new detection and regression estimation n作者: exorbitant 時間: 2025-3-22 03:22 作者: 貿(mào)易 時間: 2025-3-22 08:14 作者: 弄皺 時間: 2025-3-22 08:56
GC-MRNet: Gated Cascade Multi-stage Regression Network for Crowd Countingproaches usually utilize deep convolutional neural network (CNN) to regress a density map from deep level features and obtained the counts. However, the best results may be obtained from the features of lower level instead of deep level. It is mainly due to the overfitting that degrades the adaptabi作者: 以煙熏消毒 時間: 2025-3-22 13:01
Latent Feature-Aware and Local Structure-Preserving Network for 3D Completion from a Single Depth Vipproaches have demonstrated promising performance, they tend to produce unfaithful and incomplete 3D shape. In this paper, we propose Latent Feature-Aware and Local Structure-Preserving Network (LALP-Net) for completing the full 3D shape from a single depth view of an object, which consists of a gen作者: 繁榮地區(qū) 時間: 2025-3-22 20:21
Facial Expression Recognition by Expression-Specific Representation Swappingns. Although significant progress has been made towards improving the expression classification, challenges due to the large variations of individuals and the lack of consistent annotated samples still remain. In this paper, we propose to disentangle facial representations into expression-specific r作者: Nonflammable 時間: 2025-3-23 00:46
Iterative Error Removal for Time-of-Flight Depth Imaginglated Continuous Wave (AMCW)-based indirect Time-of-Flight (ToF) has been widely used in recent years. Unfortunately, the depth acquired by ToF sensors is often corrupted by imaging noise, multi-path interference (MPI), and low intensity. Different methods have been proposed for tackling these issue作者: 刀鋒 時間: 2025-3-23 04:47 作者: 胎兒 時間: 2025-3-23 08:21
Learning How to Zoom In: Weakly Supervised ROI-Based-DAM for Fine-Grained Visual Classificationta. How to efficiently localize the subtle but discriminative features with limited data is not straightforward. In this paper, we propose a simple yet efficient region of interest based data augmentation method (ROI-based-DAM) to handle the circumstance. The proposed ROI-based-DAM can first localiz作者: DAMN 時間: 2025-3-23 11:03
(Input) Size Matters for CNN Classifiers that fully convolutional image classifiers are not agnostic to the input size but rather show significant differences in performance: presenting the same image at different scales can result in different outcomes. A closer look reveals that there is no simple relationship between input size and mod作者: frivolous 時間: 2025-3-23 14:29
Accelerating Depthwise Separable Convolutions with Vector Processornted hardware accelerators are outstanding in terms of saving resources and energy. However, lightweight networks designed for small processors do not perform efficiently on these accelerators. Moreover, there are too many models to design an application-specific circuit for each model. In this work作者: Hyperlipidemia 時間: 2025-3-23 19:48 作者: 隱語 時間: 2025-3-24 01:31
Deep Unitary Convolutional Neural Networksignals either amplify or attenuate across the layers and become saturated. While other normalization methods aim to fix the stated problem, most of them have inference speed penalties in those applications that require running averages of the neural activations. Here we extend the unitary framework 作者: 步兵 時間: 2025-3-24 05:23 作者: GRIEF 時間: 2025-3-24 06:49 作者: 上下倒置 時間: 2025-3-24 13:08
,Me?vorrichtungen und Me?automaten,ns to collect features from certain levels of the feature hierarchy, and do not consider the significant differences among them. We propose a better architecture of feature pyramid networks, named selective multi-scale learning (SMSL), to address this issue. SMSL is efficient and general, which can 作者: esthetician 時間: 2025-3-24 18:38
,Me?mikroskop und Profilprojektor,heir comparable results, most of these counting methods disregard the fact that crowd density varies enormously in the spatial and temporal domains of videos. This thus hinders the improvement in performance of video crowd counting. To overcome that issue, a new detection and regression estimation n作者: Decimate 時間: 2025-3-24 22:05
https://doi.org/10.1007/978-3-322-96810-4vision problems in the most diverse areas. However, this type of approach requires a large number of samples of the problem to be treated, which often makes this type of approach difficult. In computer vision applications aimed at fruit growing, this problem is even more noticeable, as the performan作者: scrutiny 時間: 2025-3-25 01:44 作者: Enteropathic 時間: 2025-3-25 05:28 作者: vocation 時間: 2025-3-25 09:23
,Me?verfahren mit koh?rentem Licht,pproaches have demonstrated promising performance, they tend to produce unfaithful and incomplete 3D shape. In this paper, we propose Latent Feature-Aware and Local Structure-Preserving Network (LALP-Net) for completing the full 3D shape from a single depth view of an object, which consists of a gen作者: 平常 時間: 2025-3-25 14:02 作者: 陳腐思想 時間: 2025-3-25 18:58 作者: 乏味 時間: 2025-3-25 23:48 作者: HALO 時間: 2025-3-26 04:09
Entwicklungstendenzen und Ausblick,ta. How to efficiently localize the subtle but discriminative features with limited data is not straightforward. In this paper, we propose a simple yet efficient region of interest based data augmentation method (ROI-based-DAM) to handle the circumstance. The proposed ROI-based-DAM can first localiz作者: 慢跑 時間: 2025-3-26 07:35
,Ausbildung in der Fertigungsme?technik, that fully convolutional image classifiers are not agnostic to the input size but rather show significant differences in performance: presenting the same image at different scales can result in different outcomes. A closer look reveals that there is no simple relationship between input size and mod作者: 窩轉(zhuǎn)脊椎動物 時間: 2025-3-26 12:23 作者: 雄辯 時間: 2025-3-26 12:43 作者: Herpetologist 時間: 2025-3-26 20:27
https://doi.org/10.1007/978-3-642-56453-6ignals either amplify or attenuate across the layers and become saturated. While other normalization methods aim to fix the stated problem, most of them have inference speed penalties in those applications that require running averages of the neural activations. Here we extend the unitary framework 作者: spondylosis 時間: 2025-3-26 22:08 作者: 上下倒置 時間: 2025-3-27 02:46
https://doi.org/10.1007/978-3-662-07197-7ng. We propose a general framework in which 6 of these variants can be interpreted as different instances of the same approach. They are the vanilla gradient descent, the classical and generalized Gauss-Newton methods, the natural gradient descent method, the gradient covariance matrix approach, and作者: Repatriate 時間: 2025-3-27 05:20 作者: slipped-disk 時間: 2025-3-27 12:36 作者: Champion 時間: 2025-3-27 14:33 作者: 有斑點 時間: 2025-3-27 18:02
Artificial Neural Networks and Machine Learning – ICANN 2021978-3-030-86340-1Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 極小量 時間: 2025-3-28 00:24 作者: anniversary 時間: 2025-3-28 02:19
https://doi.org/10.1007/978-3-642-56453-6square matrices. Our proposed unitary convolutional neural networks deliver up to 32% faster inference speeds and up to 50% reduction in permanent hard disk space while maintaining competitive prediction accuracy.作者: 涂掉 時間: 2025-3-28 07:43 作者: 啞巴 時間: 2025-3-28 12:15 作者: 震驚 時間: 2025-3-28 15:01
Conference proceedings 2021Artificial Neural Networks, ICANN 2021, held in Bratislava, Slovakia, in September 2021.* The total of 265 full papers presented in these proceedings was carefully reviewed and selected from 496 submissions, and organized in 5 volumes..In this volume, the papers focus on topics such as computer visi作者: Nausea 時間: 2025-3-28 18:46
0302-9743 g, explainable methods, few-shot learning and generative adversarial networks. . .*The conference was held online 2021 due to the COVID-19 pandemic..978-3-030-86339-5978-3-030-86340-1Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Infinitesimal 時間: 2025-3-29 01:41 作者: Licentious 時間: 2025-3-29 03:49
Entwicklungstendenzen und Ausblick,plemented in the standard training and inference phases to boost the fined-grained classification accuracy. Our experimental results on extensive FGVC benchmark datasets show that the baseline model such as ResNeXt-50 can achieve competitive state-of-the-art performance by utilizing the proposed ROI-based-DAM, which demonstrate its effectiveness.作者: 有斑點 時間: 2025-3-29 08:05 作者: PAGAN 時間: 2025-3-29 15:22 作者: Exhilarate 時間: 2025-3-29 19:05
Learning How to Zoom In: Weakly Supervised ROI-Based-DAM for Fine-Grained Visual Classificationplemented in the standard training and inference phases to boost the fined-grained classification accuracy. Our experimental results on extensive FGVC benchmark datasets show that the baseline model such as ResNeXt-50 can achieve competitive state-of-the-art performance by utilizing the proposed ROI-based-DAM, which demonstrate its effectiveness.作者: 虛弱的神經(jīng) 時間: 2025-3-29 21:40 作者: 百科全書 時間: 2025-3-30 01:45
0302-9743 erence on Artificial Neural Networks, ICANN 2021, held in Bratislava, Slovakia, in September 2021.* The total of 265 full papers presented in these proceedings was carefully reviewed and selected from 496 submissions, and organized in 5 volumes..In this volume, the papers focus on topics such as com作者: RAG 時間: 2025-3-30 06:38 作者: 和音 時間: 2025-3-30 11:45
First-Order and Second-Order Variants of the Gradient Descent in a Unified Frameworkradient descent, the classical and generalized Gauss-Newton methods, the natural gradient descent method, the gradient covariance matrix approach, and Newton’s method. Besides interpreting these methods within a single framework, we explain their specificities and show under which conditions some of them coincide.作者: 無關(guān)緊要 時間: 2025-3-30 15:38
,Me?vorrichtungen und Me?automaten,be integrated in both single-stage and two-stage detectors to boost detection performance, with nearly no extra inference cost. RetinaNet combined with SMSL obtains 1.8% improvement in AP (from 39.1% to 40.9%) on COCO dataset. When integrated with SMSL, two-stage detectors can get around 1.0% improvement in AP.作者: 雇傭兵 時間: 2025-3-30 18:36 作者: Stricture 時間: 2025-3-30 22:53 作者: 相信 時間: 2025-3-31 01:57 作者: 暴發(fā)戶 時間: 2025-3-31 06:25
https://doi.org/10.1007/978-3-322-96810-4of an unparalleled size in the literature, with the main diseases and damages of papaya fruit (.). The proposed data set in this work consists of 15,179 RGB images duly and manually annotated with the position of the fruit and the disease/damage found within it..In order to validate our dataset, we 作者: Oration 時間: 2025-3-31 11:05
,Grundlagen der Fertigungsme?technik, regressors in different levels. Then, the features derived from the density map were cascaded to assist generating a higher quality density map in next stage. Finally, the gated blocks were designed to achieve the controllable information interaction between cascade and backbone. Extensive experime作者: 細微的差異 時間: 2025-3-31 15:35 作者: Antarctic 時間: 2025-3-31 19:26
