標(biāo)題: Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2023; 32nd International C Lazaros Iliadis,Antonios Papaleonidas,Chrisina Jay Confe [打印本頁] 作者: chondrocyte 時(shí)間: 2025-3-21 17:53
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2023影響因子(影響力)
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2023影響因子(影響力)學(xué)科排名
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2023網(wǎng)絡(luò)公開度
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2023網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2023被引頻次
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2023被引頻次學(xué)科排名
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2023年度引用
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2023年度引用學(xué)科排名
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2023讀者反饋
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2023讀者反饋學(xué)科排名
作者: defibrillator 時(shí)間: 2025-3-21 23:54
Artificial Neural Networks and Machine Learning – ICANN 202332nd International C作者: 毗鄰 時(shí)間: 2025-3-22 03:36 作者: 合唱隊(duì) 時(shí)間: 2025-3-22 05:18
New Directions in Welfare Historyonfusing classes can be increased by simply using label smoothing. Extensive experiments conducted on three popular fine-grained benchmarks demonstrate that we achieve . performance. Meanwhile, during the inference, our method requires less computational burden.作者: aneurysm 時(shí)間: 2025-3-22 10:56
https://doi.org/10.1007/978-3-031-26024-7inement network module (MrNet) to estimate the refined displacement map with features from different layers and different domains (i.e. coarse displacement images and RGB images). Finally, we design a novel normal smoothing loss that improves the reconstructed details and realisticity. Extensive exp作者: Water-Brash 時(shí)間: 2025-3-22 14:55 作者: Anticonvulsants 時(shí)間: 2025-3-22 17:54 作者: 最有利 時(shí)間: 2025-3-22 23:27 作者: 使顯得不重要 時(shí)間: 2025-3-23 05:01 作者: 噴油井 時(shí)間: 2025-3-23 06:49 作者: 收藏品 時(shí)間: 2025-3-23 11:32
Fertilit?tsst?rungen beim Manneon aerial view, and the experimental results show that our model significantly improves the segmentation performance of special lanes and lane lines. Additionally, it achieves the highest mIoU (mean Intersection over Union) of 86.4% while having 28.9M parameters.作者: Promotion 時(shí)間: 2025-3-23 16:16 作者: 背帶 時(shí)間: 2025-3-23 21:41
Fusion of the Sperm with the Vitellus,omplementary relationship between splicing regions and their boundaries. Thirdly, in order to achieve more precise positioning results, SCU is used as postprocessing for removing false alarm pixels outside the focusing regions. In addition, we propose an adaptive loss weight adjustment algorithm to 作者: 性滿足 時(shí)間: 2025-3-23 23:04
https://doi.org/10.1007/978-1-4684-4016-4tion. Extensive experimental results on MS COCO dataset demonstrate the effectiveness of our method and each proposed module, which can obtain 40.6 BLEU-4 and 135.6 CIDEr scores. Code will be released in the final version of the paper.作者: 枕墊 時(shí)間: 2025-3-24 03:24
Fuller W. Bazer,M. H. Goldstein,D. H. Barronsses. The alignment loss is introduced to minimize the sample-level distribution differences of teacher-student models in the common representation space. Furthermore, the student learns heterogeneous unsupervised classification tasks through soft targets efficiently and flexibly in the task-level a作者: laparoscopy 時(shí)間: 2025-3-24 10:20 作者: Jubilation 時(shí)間: 2025-3-24 10:52
,A Data Augmentation Based ViT for?Fine-Grained Visual Classification,onfusing classes can be increased by simply using label smoothing. Extensive experiments conducted on three popular fine-grained benchmarks demonstrate that we achieve . performance. Meanwhile, during the inference, our method requires less computational burden.作者: FRET 時(shí)間: 2025-3-24 17:53
,A Detail Geometry Learning Network for?High-Fidelity Face Reconstruction,inement network module (MrNet) to estimate the refined displacement map with features from different layers and different domains (i.e. coarse displacement images and RGB images). Finally, we design a novel normal smoothing loss that improves the reconstructed details and realisticity. Extensive exp作者: Diverticulitis 時(shí)間: 2025-3-24 20:04 作者: Interferons 時(shí)間: 2025-3-25 02:42 作者: mettlesome 時(shí)間: 2025-3-25 07:22
,An Auxiliary Modality Based Text-Image Matching Methodology for?Fake News Detection,ists of four components: one fusion module and three matching modules, where the former one joints text and image features, and the latter three computes the corresponding similarities among textual, visual, and auxiliary modalities. Aligning them with different weights, and connecting them with a c作者: HAUNT 時(shí)間: 2025-3-25 10:15
An Improved YOLOv5 with Structural Reparameterization for Surface Defect Detection,rk to focus on detecting small targets. The experimental results on the Northeastern University (NEU) surface defect database show that, our model is superior to the state-of-the-art detectors, such as the original YOLOv5, Fast-RCNN in accuracy and speed.作者: 雜役 時(shí)間: 2025-3-25 14:49
ASP Loss: Adaptive Sample-Level Prioritizing Loss for Mass Segmentation on Whole Mammography Imagesvery sample, to prioritize the contribution of each loss term accordingly. As one of the variations of U-Net, AU-Net is selected as the baseline approach for the evaluation of the proposed loss. The ASP loss could be integrated with other existing mass segmentation approaches to enhance their perfor作者: 確定無疑 時(shí)間: 2025-3-25 17:30 作者: libertine 時(shí)間: 2025-3-25 22:33 作者: 吹牛者 時(shí)間: 2025-3-26 00:54
,Combining Edge-Guided Attention and?Sparse-Connected U-Net for?Detection of?Image Splicing,omplementary relationship between splicing regions and their boundaries. Thirdly, in order to achieve more precise positioning results, SCU is used as postprocessing for removing false alarm pixels outside the focusing regions. In addition, we propose an adaptive loss weight adjustment algorithm to 作者: 劇本 時(shí)間: 2025-3-26 06:40
,Contour-Augmented Concept Prediction Network for?Image Captioning,tion. Extensive experimental results on MS COCO dataset demonstrate the effectiveness of our method and each proposed module, which can obtain 40.6 BLEU-4 and 135.6 CIDEr scores. Code will be released in the final version of the paper.作者: 議程 時(shí)間: 2025-3-26 10:39 作者: Anterior 時(shí)間: 2025-3-26 13:12 作者: defibrillator 時(shí)間: 2025-3-26 17:08 作者: badinage 時(shí)間: 2025-3-27 00:10 作者: 失望昨天 時(shí)間: 2025-3-27 01:06 作者: Tracheotomy 時(shí)間: 2025-3-27 06:20 作者: 良心 時(shí)間: 2025-3-27 10:50
https://doi.org/10.1007/978-1-4020-9682-2 expression and the study of cell function. Existing cell segmentation methods have drawbacks in terms of inaccurate location of segmentation boundary, misidentification, and inaccurate segmentation of overlapping cells. To address these issues, a novel . (MMCS) is proposed in this paper. Our motiva作者: malapropism 時(shí)間: 2025-3-27 13:52 作者: 積習(xí)已深 時(shí)間: 2025-3-27 17:46
https://doi.org/10.1057/9780333981344ology. As social networks gradually presents a multimodal property, many scholars have devoted to multimodal fake news detection. However, the current multimodal achievements mainly focus on the fusion modeling between texts and images, while their consistencies are still in their infancy. This pape作者: Repetitions 時(shí)間: 2025-3-28 01:48
Christianization in Ovamboland,s, remains challenging due to the substantial computational demands of existing object detection models. In this paper, we propose an improved remote sensing image small object detection method based on YOLOv5. In order to preserve high-resolution features, we remove the Focus module from the YOLOv5作者: 協(xié)定 時(shí)間: 2025-3-28 03:21
https://doi.org/10.1007/978-3-658-15811-8g methods for detecting surface defects cannot meet the requirements in terms of speed and accuracy. Based on structural re-parameterization, coordinate attention (CA) mechanism, and an additional detection head, we propose an improved YOLOv5 model for detecting surface defects of steel plates. Firs作者: ASSET 時(shí)間: 2025-3-28 08:18 作者: Ornament 時(shí)間: 2025-3-28 14:05
,Die künstliche Samenübertragung,els often appear to have poor appearance when viewed from a new perspective. We thus propose a new method that requires only images and their silhouettes to accurately predict the shape of birds, as well as to obtain reasonable appearance in new perspectives. The key to the method lies in the introd作者: 傷心 時(shí)間: 2025-3-28 18:23
Fertilit?tsst?rungen beim Manneification lane semantic segmentation suffer from low segmentation accuracy for special lanes (e.g., ramp, emergency lane) and lane lines. To address this problem, we propose a cross-layer multi-class lane semantic segmentation model called CLASPPNet (Cross-Layer Atrous Spatial Pyramid Pooling Networ作者: 我正派 時(shí)間: 2025-3-28 22:25
Fertilization Mechanisms in Man and Mammalsn deep learning with excellent performance, but their memory and computation costs hinder practical applications. In this paper, we propose a down-up sampling continuous mutual affine super-resolution network (DUSCMAnet) to solve above problems. Moreover, we propose a classification-based SR algorit作者: aggrieve 時(shí)間: 2025-3-29 01:03
Fusion of the Sperm with the Vitellus,methods for detecting and locating such tampering. Previous studies have mainly focused on the supervisory role of the mask on the model. The mask edges contain rich complementary signals, which help to fully understand the image and are usually ignored. In this paper, we propose a new network named作者: interrupt 時(shí)間: 2025-3-29 04:15 作者: Intact 時(shí)間: 2025-3-29 08:23 作者: mechanical 時(shí)間: 2025-3-29 12:38
N. Bagni,A. Tassoni,M. Franceschettid domain adaptation is proved to be effective on this problem in recent researches. Unsupervised domain adaptive object detection of students’ heads between different classrooms has becoming an important task with the development of Smart Classroom. However, few cross-classroom models for students’ 作者: GRAIN 時(shí)間: 2025-3-29 16:24
N. Bagni,A. Tassoni,M. Franceschettiing text-driven image manipulation is typically implemented by GAN inversion or fine-tuning diffusion models. The former is limited by the inversion capability of GANs, which fail to reconstruct pictures with novel poses and perspectives. The latter methods require expensive optimization for each in作者: Obloquy 時(shí)間: 2025-3-29 20:16 作者: infinite 時(shí)間: 2025-3-30 01:16
https://doi.org/10.1007/978-3-031-44210-0artificial neural networks (NN); machine learning; deep learning; federated learning; convolutional neur作者: Compass 時(shí)間: 2025-3-30 04:45 作者: 體貼 時(shí)間: 2025-3-30 11:12
Artificial Neural Networks and Machine Learning – ICANN 2023978-3-031-44210-0Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 冷漠 時(shí)間: 2025-3-30 14:45 作者: 無畏 時(shí)間: 2025-3-30 17:35 作者: 山崩 時(shí)間: 2025-3-30 22:31
,A Lightweight Multi-Scale Large Kernel Attention Hierarchical Network for?Single Image Deraining,. To address this issue, we propose a lightweight multi-scale large kernel attention hierarchical network(LMANet). Our approach combines multi-scale and Large Kernel Attention(LKA) to create Multi-Scale Large Kernel Attention (MSLKA), where large kernel decomposition can effectively decouple large k作者: monologue 時(shí)間: 2025-3-31 02:37
A Multi-scale Method for Cell Segmentation in Fluorescence Microscopy Images, expression and the study of cell function. Existing cell segmentation methods have drawbacks in terms of inaccurate location of segmentation boundary, misidentification, and inaccurate segmentation of overlapping cells. To address these issues, a novel . (MMCS) is proposed in this paper. Our motiva作者: 整體 時(shí)間: 2025-3-31 08:51 作者: 笨拙的你 時(shí)間: 2025-3-31 11:07 作者: BLOT 時(shí)間: 2025-3-31 16:57
,An Improved Lightweight YOLOv5 for?Remote Sensing Images,s, remains challenging due to the substantial computational demands of existing object detection models. In this paper, we propose an improved remote sensing image small object detection method based on YOLOv5. In order to preserve high-resolution features, we remove the Focus module from the YOLOv5作者: 吞沒 時(shí)間: 2025-3-31 18:42
An Improved YOLOv5 with Structural Reparameterization for Surface Defect Detection,g methods for detecting surface defects cannot meet the requirements in terms of speed and accuracy. Based on structural re-parameterization, coordinate attention (CA) mechanism, and an additional detection head, we propose an improved YOLOv5 model for detecting surface defects of steel plates. Firs作者: deadlock 時(shí)間: 2025-4-1 00:46 作者: 內(nèi)疚 時(shí)間: 2025-4-1 02:24 作者: 胡言亂語 時(shí)間: 2025-4-1 07:48 作者: modifier 時(shí)間: 2025-4-1 11:27 作者: fiscal 時(shí)間: 2025-4-1 15:33
,Combining Edge-Guided Attention and?Sparse-Connected U-Net for?Detection of?Image Splicing,methods for detecting and locating such tampering. Previous studies have mainly focused on the supervisory role of the mask on the model. The mask edges contain rich complementary signals, which help to fully understand the image and are usually ignored. In this paper, we propose a new network named作者: 明確 時(shí)間: 2025-4-1 21:53
,Contour-Augmented Concept Prediction Network for?Image Captioning,n an image, making the model unable to accurately capture visual semantics, and further making the generated descriptions irrelevant to the content of the given image. Thus, in this paper, we propose a Contour-augmented Concept Prediction Network (CCP-Net), which leverages two additional aspects of
