標(biāo)題: Titlebook: Advanced Intelligent Computing Technology and Applications; 20th International C De-Shuang Huang,Yijie Pan,Jiayang Guo Conference proceedin [打印本頁] 作者: Filament 時間: 2025-3-21 18:22
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書目名稱Advanced Intelligent Computing Technology and Applications被引頻次
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書目名稱Advanced Intelligent Computing Technology and Applications讀者反饋
書目名稱Advanced Intelligent Computing Technology and Applications讀者反饋學(xué)科排名
作者: 大量殺死 時間: 2025-3-21 21:47 作者: 創(chuàng)作 時間: 2025-3-22 01:43
Mechatronische Fahrwerkregelung,lation experiments, ablation experiments, and generalization experiments. Our model is state-of-the-art and achieves a maximum improvement of 13.07% relative to the baseline on the similar set, particularly on the most challenging novel set with a maximum improvement of 4.06%. In practical experimen作者: 辮子帶來幫助 時間: 2025-3-22 06:53
https://doi.org/10.1007/978-3-8348-9573-8s feature aggregation across multiple scales, particularly benefiting the detection of small objects. We assessed the effectiveness of TC-YOLO on our traffic camera detection dataset, and the results obtained from the experiment substantiate the efficacy of our model. The model attained a mean perfo作者: Compatriot 時間: 2025-3-22 11:07 作者: 系列 時間: 2025-3-22 15:14 作者: restrain 時間: 2025-3-22 19:51
https://doi.org/10.1007/978-3-642-95399-6s. Despite being trained under category supervision, the SGCA module adeptly captures active regions encompassing various high-order semantic features. These features include focusing features and contour features of the object. When integrated into existing lightweight backbone networks, the SGCA m作者: Malleable 時間: 2025-3-23 00:21
https://doi.org/10.1007/978-3-642-95399-6ferent receptive fields and extract multi-scale feature representations. The second is to use the serialized maximum pooling structure to aggregate the local information of the image. By considering the pixels, regions and features around the target, the relationship between the target and its surro作者: Permanent 時間: 2025-3-23 03:43 作者: 疏忽 時間: 2025-3-23 05:43
Vom Kontenrahmen zum Kontenplan,KG with popular adversarial robustness methods. Experiment evaluations on three benchmark databases demonstrate that our proposed attention-aware knowledge guidance for deep metric learning significantly outperforms state-of-the-art defenses in terms of both adversarial robustness and benign perform作者: 率直 時間: 2025-3-23 10:54 作者: infantile 時間: 2025-3-23 16:42
https://doi.org/10.1007/978-3-322-82874-3ution decomposes 3D convolutions into 2D and 1D channels, which can effectively reduce the number of parameters. Compared to traditional deep learning models, experimental results have shown the proposed model is superior to others in performance on several benchmark datasets.作者: Fester 時間: 2025-3-23 19:25 作者: 參考書目 時間: 2025-3-24 00:29 作者: CAB 時間: 2025-3-24 05:33 作者: SUE 時間: 2025-3-24 08:23 作者: grotto 時間: 2025-3-24 13:29 作者: 支形吊燈 時間: 2025-3-24 14:53
,?konomische Theorie der Politik,a fixed feature extractor and training the generator to produce samples with a more uniform distribution of identity. In both our own framework and that of others, it can provide stronger constraints. Meanwhile, we design an Explicit Decoupling Network (EDN) along with its corresponding guiding func作者: hegemony 時間: 2025-3-24 20:16 作者: Arbitrary 時間: 2025-3-24 23:19 作者: 芳香一點 時間: 2025-3-25 05:30 作者: INTER 時間: 2025-3-25 10:36
Controlling Attention Map Better for Text-Guided Image Editing Diffusion Modelsdomains, there exists a lack of a unified method that integrates different editing approaches. This absence impedes users from choosing an algorithm that best suits their needs. To address this, we propose a convergent attention map modification framework. This framework seamlessly integrates variou作者: 助記 時間: 2025-3-25 15:35 作者: 發(fā)酵劑 時間: 2025-3-25 16:44 作者: 舊石器 時間: 2025-3-25 20:12 作者: 好開玩笑 時間: 2025-3-26 00:21
Enhancing Adversarial Robustness for Deep Metric Learning via Attention-Aware Knowledge GuidanceKG with popular adversarial robustness methods. Experiment evaluations on three benchmark databases demonstrate that our proposed attention-aware knowledge guidance for deep metric learning significantly outperforms state-of-the-art defenses in terms of both adversarial robustness and benign perform作者: irritation 時間: 2025-3-26 04:38
IMFA-Stereo: Domain Generalized Stereo Matching via Iterative Multimodal Feature Aggregation Cost Vofine the initial disparity estimation and consolidate the aggregated cost volume, resulting in an accurate disparity map. Comprehensive experiments demonstrate that IMFA-Stereo achieves state-of-the-art stereo matching performance and excels in cross-domain generalization when trained on Scene Flow 作者: Obstacle 時間: 2025-3-26 08:48 作者: 禁令 時間: 2025-3-26 13:16 作者: cliche 時間: 2025-3-26 18:03
DSMENet: A Road Segmentation Network Based on Dual-Branch Dynamic Snake Convolutional Encoding and Mich are then input into the decoder for spatial resolution restoration. Finally, a multi-modal information iterative enhancement module is embedded at the end of the network to fully exploit spatial detail features of original multi-modal data and enhance the features at the end of the de-coder, the作者: 圓錐 時間: 2025-3-27 00:12
MPRNet: Multi-scale Pointwise Regression Network for Crowd Counting and Localizationi-scale feature extractor (MFE) and a feature fusion algorithm of regional maximum substitution (RMS) to enhance feature extraction and fusion capabilities. Additionally, we employ the Hungarian algorithm for one-to-one matching of predicted and ground truth points, ensuring high quality location in作者: Flat-Feet 時間: 2025-3-27 04:20
Text-to-Image Generation with Multiscale Semantic Context-Aware Generative Adversarial Networkscess, encompassing global to detailed aspects. Furthermore, the CrossBlock Context Aware Encoding (CCAE) module explicitly establishes semantic context across different synthesis blocks during the local feature delivery. Finally, MSCA-GAN introduces an additional CLIP guidance term to verify the sem作者: Concerto 時間: 2025-3-27 07:15
CHMF: Colorful Human Reconstruction Based on Mesh Featuress in terms of color but also eliminates color errors caused by limb occlusion due to human movement, a limitation in existing methods. We evaluate our method on the Thuman2.0 dataset, and extensive experiments have shown that our approach outperforms previous methods both qualitatively and quantitat作者: 不安 時間: 2025-3-27 09:32
Face Swapping via Reverse Contrastive Learning and Explicit Identity-Attribute Disentanglementa fixed feature extractor and training the generator to produce samples with a more uniform distribution of identity. In both our own framework and that of others, it can provide stronger constraints. Meanwhile, we design an Explicit Decoupling Network (EDN) along with its corresponding guiding func作者: conscribe 時間: 2025-3-27 17:06 作者: Servile 時間: 2025-3-27 20:32
Generating Graph-Based Rules for Enhancing Logical Reasoningh reasoning, significantly improves inference capabilities and showcases the potential of graph-based rules in inductive KGC. To demonstrate the effectiveness of the GRELR framework, we conduct experiments on three benchmark datasets, and our approach achieves state-of-the-art performance.作者: 學(xué)術(shù)討論會 時間: 2025-3-27 23:45
Conference proceedings 2024ons. Therefore, the theme for this conference was "Advanced Intelligent Computing Technology and Applications". Papers that focused on this theme were solicited, addressing theories, methodologies, and applications in science and technology...?.作者: Thrombolysis 時間: 2025-3-28 03:44
Conference proceedings 2024 refereed proceedings of the 20th International Conference on Intelligent Computing, ICIC 2024, held in Tianjin, China, during August 5-8, 2024...The total of 863 regular papers were carefully reviewed and selected from 2189 submissions...This year, the conference concentrated mainly on the theories作者: amygdala 時間: 2025-3-28 08:49
0302-9743 4882 - the refereed proceedings of the 20th International Conference on Intelligent Computing, ICIC 2024, held in Tianjin, China, during August 5-8, 2024...The total of 863 regular papers were carefully reviewed and selected from 2189 submissions...This year, the conference concentrated mainly on th作者: Mri485 時間: 2025-3-28 13:45
Multi-gait Synthesis Based on Convolutional Neural Networks on our lab’s self-collected multi-person gait dataset have shown that our model can generate satisfactory multi-person gait sequences. Furthermore, testing the generated gait images with the GaitSet model for gait recognition demonstrates that the image quality produced by our model is acceptable.作者: 調(diào)味品 時間: 2025-3-28 15:23 作者: Minuet 時間: 2025-3-28 20:33
Mechatronische Fahrwerkregelung,roposed modules, including Gaussian Noise Mix (GNM), Resblock, and Local Features Interpolation (LFI), use GSNet as the baseline. GNM is used for feature augmentation of backbone features during training to reduce the empirical risk of the model when dealing with novel samples. Resblock is designed 作者: Palliation 時間: 2025-3-29 00:07
https://doi.org/10.1007/978-3-8348-9573-8, the pixel resolution of images captured by traffic cameras is generally low, and there is a significant difference between the data collected during daytime and nighttime. Existing object detection algorithms don’t perform admirably on low-resolution images. To overcome these challenges, we presen作者: 嘲弄 時間: 2025-3-29 04:39 作者: GUEER 時間: 2025-3-29 10:05
Konzepte und Kennfelder von Antrieben the single gait forms of each participant. Our goal is to utilize a dual-branch input pipeline, where each separate branch learns the gait features of each individual, aggregating the gait sequences of two different individuals to generate a complete dual-person gait sequence. Experiments conducted作者: 絕緣 時間: 2025-3-29 12:30 作者: 中止 時間: 2025-3-29 16:33
https://doi.org/10.1007/978-3-642-95399-6or image classification tasks, resulting in performance degradation. Attention mechanisms can effectively improve the expressiveness of models, but most attention modules in recent studies are designed to be complex to achieve better performance. We expect to learn high-level semantic features with 作者: 技術(shù) 時間: 2025-3-29 23:37 作者: 廢除 時間: 2025-3-30 02:48 作者: 細(xì)胞學(xué) 時間: 2025-3-30 07:44
Vom Kontenrahmen zum Kontenplan,els. Existing defense methods employ adversarial triplets to improve adversarial robustness but sacrifice benign performance. In this paper, we propose a novel framework for deep metric learning by introducing the concept of “Attention-Aware Knowledge Guidance”, dubbed AAKG, which not only enhances 作者: 中子 時間: 2025-3-30 10:15
Systematik der Finanzierungsformen, progressively supplanted cost filtering-based methods due to their efficacy in handling extensive scenes. However, they manifest sensitivity to noise and encounter challenges in addressing local blurring issues due to insufficient feature encoding. Consequently, we propose IMFA-Stereo, a hierarchic作者: Onerous 時間: 2025-3-30 16:03 作者: 形容詞 時間: 2025-3-30 17:33
Grundlagen der Finanzierungspraxisral Networks (GNNs) to learn and propagate entity representations, achieving notable performance. However, these approaches primarily focus on chain-based logical rules, limiting their ability to capture the rich semantics of knowledge graphs. To address this challenge, we propose to generate .raph-作者: 夾克怕包裹 時間: 2025-3-30 21:33
https://doi.org/10.1007/978-3-663-13355-1complex environment of the underground coal mine and the variability of object poses, the general object detection algorithms cannot provide good performance. Hence, an improved underground multi-pose object detection method named YOLO-PR has been proposed. An EPA mechanism is designed to improve th作者: hemophilia 時間: 2025-3-31 02:35 作者: Embolic-Stroke 時間: 2025-3-31 06:56
Arithmetische und geometrischen Folgen,ution of crowd, and subsequently infer the total number of people by integrating across the density map. However, this approach heavily relies on Gaussian kernels for generating density maps. Furthermore, due to the unknown size of targets, it’s challenging to pre-set Gaussian kernel parameters prop作者: 機制 時間: 2025-3-31 12:03 作者: Inertia 時間: 2025-3-31 14:35
https://doi.org/10.1007/b138519 methods have not made use of the reconstructed 3D human body features to aid color restoration, leading to less-than-optimal results. We propose a new method called CHMF in this paper. Unlike previous works, this method fully exploits the inherent features of the 3D human body for color restoration
