formation
发表于 2025-3-21 18:50:14
书目名称Artificial Neural Networks and Machine Learning – ICANN 2021影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0162651<br><br> <br><br>书目名称Artificial Neural Networks and Machine Learning – ICANN 2021影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0162651<br><br> <br><br>书目名称Artificial Neural Networks and Machine Learning – ICANN 2021网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0162651<br><br> <br><br>书目名称Artificial Neural Networks and Machine Learning – ICANN 2021网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0162651<br><br> <br><br>书目名称Artificial Neural Networks and Machine Learning – ICANN 2021被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0162651<br><br> <br><br>书目名称Artificial Neural Networks and Machine Learning – ICANN 2021被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0162651<br><br> <br><br>书目名称Artificial Neural Networks and Machine Learning – ICANN 2021年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0162651<br><br> <br><br>书目名称Artificial Neural Networks and Machine Learning – ICANN 2021年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0162651<br><br> <br><br>书目名称Artificial Neural Networks and Machine Learning – ICANN 2021读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0162651<br><br> <br><br>书目名称Artificial Neural Networks and Machine Learning – ICANN 2021读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0162651<br><br> <br><br>
条约
发表于 2025-3-21 23:08:02
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
exorbitant
发表于 2025-3-22 03:22:43
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贸易
发表于 2025-3-22 08:14:52
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弄皱
发表于 2025-3-22 08:56:12
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
以烟熏消毒
发表于 2025-3-22 13:01:48
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
繁荣地区
发表于 2025-3-22 20:21:07
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
Nonflammable
发表于 2025-3-23 00:46:35
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
刀锋
发表于 2025-3-23 04:47:45
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胎儿
发表于 2025-3-23 08:21:40
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