標(biāo)題: Titlebook: Advances in Visual Computing; 15th International S George Bebis,Zhaozheng Yin,George Baciu Conference proceedings 2020 Springer Nature Swit [打印本頁] 作者: incompatible 時間: 2025-3-21 16:10
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)公開度
書目名稱Advances in Visual Computing網(wǎng)絡(luò)公開度學(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: 時代 時間: 2025-3-21 23:34
Self-Competitive Neural NetworksNN tries to improve its accuracy by competing with itself (generating hard samples and then learning them), the technique is called Self-Competitive Neural Network (SCNN). To generate such samples, we pose the problem as an optimization task, where the network weights are fixed and use a gradient de作者: 增長 時間: 2025-3-22 04:02 作者: NEG 時間: 2025-3-22 08:12 作者: consent 時間: 2025-3-22 09:08 作者: prediabetes 時間: 2025-3-22 13:10 作者: 缺陷 時間: 2025-3-22 20:51
Depthwise Separable Convolutions and Variational Dropout within the context of YOLOv3chnique that finds individual and unbounded dropout rates for each neural network weight. Experiments on the PASCAL VOC benchmark dataset show promising results where variational dropout combined with the most efficient YOLOv3 variant lead to an extremely sparse solution that reduces 95% of the base作者: 凝視 時間: 2025-3-22 21:24 作者: AER 時間: 2025-3-23 03:50
Towards Optimal Ship Navigation Using Image Processingh has some practical and computational limitations; however, the future unmanned vessels could benefit from the improved applications of this route optimization approach in terms of energy consumption, time, and workforce.作者: 錫箔紙 時間: 2025-3-23 07:28 作者: inquisitive 時間: 2025-3-23 11:42
Referenced Based Color Transfer for Medical Volume Renderinge reference image recommendation system to aid for selection of good reference images. With our approach, we successfully perform colored medical volume visualization and essentially eliminate the painstaking process of user interaction with a transfer function to obtain color parameters for volume 作者: 本土 時間: 2025-3-23 16:49
0302-9743 ng..Part II: object recognition/detection/categorization; 3D reconstruction; medical image analysis; vision for robotics; statistical pattern recognition; posters.978-3-030-64555-7978-3-030-64556-4Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: implore 時間: 2025-3-23 22:01
https://doi.org/10.1007/978-3-030-10871-7. Our results both qualitatively show and empirically quantify the amount of protection and stability sparse representations lend to machine learning robustness in the context of adversarial examples and class attribution.作者: 閃光東本 時間: 2025-3-24 00:51 作者: 政府 時間: 2025-3-24 06:09 作者: 裂口 時間: 2025-3-24 08:36 作者: 相互影響 時間: 2025-3-24 14:18
Syeda Tahmina Tasnim,Humayra Alamferent approach that can challenge deep learning without the effects of adversarial attacks. The first one has not been solved yet, and adversarial attacks have become even more complex to defend. Therefore, this work presents a Deep Genetic Programming method, called Brain Programming, that compete作者: 使閉塞 時間: 2025-3-24 15:01
Fatima Zahra Boughanem,Etienne Wolffhe image of a cat is easy to identify, we consider cat sketches selected from the . data set. This paper compares the proposed model with the original Sketch-RNN on 75K human-drawn cat sketches. The result indicates that our model produces sketches with higher quality than human’s sketches.作者: infatuation 時間: 2025-3-24 21:16
Conservation of Architectural Heritage (CAH)chnique that finds individual and unbounded dropout rates for each neural network weight. Experiments on the PASCAL VOC benchmark dataset show promising results where variational dropout combined with the most efficient YOLOv3 variant lead to an extremely sparse solution that reduces 95% of the base作者: fetter 時間: 2025-3-25 02:53 作者: MOAN 時間: 2025-3-25 06:01 作者: 使顯得不重要 時間: 2025-3-25 07:53
Ramagopal V. S. Uppaluri,Latha Rangan like SqueezeNet in terms of inference runtime and segmentation performance. Moreover, a pipeline for creating photo-realistic frame samples to build a self-generated dataset is introduced and used in the training and validation phase. This dataset consists of 15000 image-mask pairs including synthe作者: aptitude 時間: 2025-3-25 13:39 作者: SHOCK 時間: 2025-3-25 16:48 作者: 盲信者 時間: 2025-3-25 23:21
Conservation of Architectural Heritage regressor operates as if it is assessing the quality of a single type of images. Test results on blur distortion across three benchmarking datasets show that the proposed model achieves promising performance competitive to the state-of-the-art simultaneously for natural scene and document images.作者: 蕨類 時間: 2025-3-26 00:29
Susmita Chakraborty,A. K. Chaurasiyatations and the Hue channel of the HSV color model. The results are presented quantitatively and qualitatively for edge detection and saliency estimation problems. The experiments were conducted on the BSDS500 and ECSSD datasets.作者: 態(tài)度暖昧 時間: 2025-3-26 07:24 作者: Leisureliness 時間: 2025-3-26 12:13 作者: 萬神殿 時間: 2025-3-26 15:59 作者: crockery 時間: 2025-3-26 19:12 作者: 飾帶 時間: 2025-3-26 23:40 作者: 諂媚于性 時間: 2025-3-27 02:24
Pixel-Level Corrosion Detection on Metal Constructions by Fusion of Deep Learning Semantic and Contois and prefabrication. Thus, we adopt a novel data projection scheme that fuses the results of color segmentation, yielding accurate but over-segmented contours of a region, with a processed area of the deep masks, resulting in high-confidence corroded pixels.作者: MINT 時間: 2025-3-27 09:10 作者: Hippocampus 時間: 2025-3-27 10:01 作者: 泰然自若 時間: 2025-3-27 14:17
Improvements on the Superpixel Hierarchy Algorithm with Applications to Image Segmentation and Salietations and the Hue channel of the HSV color model. The results are presented quantitatively and qualitatively for edge detection and saliency estimation problems. The experiments were conducted on the BSDS500 and ECSSD datasets.作者: Modicum 時間: 2025-3-27 21:10
Conference proceedings 2020which was supposed to be held in San Diego, CA, USA in October 2020, took place virtually instead due to the COVID-19 pandemic...The 114 full and 4 short papers presented in these volumes were carefully reviewed and selected from 175 submissions. The papers are organized into the following topical s作者: Classify 時間: 2025-3-27 23:48
Regularization and Sparsity for Adversarial Robustness and Stable Attribution can match or exceed human-level performance in difficult image recognition tasks. However, recent research has raised a number of critical questions about the robustness and stability of these deep learning architectures. Specifically, it has been shown that they are prone to adversarial attacks, i作者: parsimony 時間: 2025-3-28 05:55 作者: OTTER 時間: 2025-3-28 07:53
A Novel Contractive GAN Model for a Unified Approach Towards Blind Quality Assessment of Images fromdels across these two types of images, where human perceptual scores and optical character recognition accuracy are the respective quality metrics. In this paper we propose a novel contractive generative adversarial model to learn a unified quality-aware representation of images from heterogeneous s作者: 虛弱的神經(jīng) 時間: 2025-3-28 12:17
Nonconvex Regularization for Network Slimming: Compressing CNNs Even Moret be deployed in low-memory devices due to its high memory requirement and computational cost. One popular, straightforward approach to compressing CNNs is network slimming, which imposes an . penalty on the channel-associated scaling factors in the batch normalization layers during training. In thi作者: faddish 時間: 2025-3-28 15:46 作者: Acclaim 時間: 2025-3-28 21:20 作者: 聯(lián)合 時間: 2025-3-29 02:23
rcGAN: Learning a Generative Model for Arbitrary Size Image Generationage used to train our model. Our two-steps method uses a randomly conditioned convolutional generative adversarial network (rcGAN) trained on patches obtained from a reference image. It can capture the reference image internal patches distribution and then produce high-quality samples that share wit作者: SNEER 時間: 2025-3-29 04:31
Sketch-Inspector: A Deep Mixture Model for High-Quality Sketch Generation of Catsen made in previous studies in this area, a relatively high proportion of the generated figures are too abstract to recognize, which illustrates that AIs fail to learn the general pattern of the target object when drawing. This paper posits that supervising the process of stroke generation can lead 作者: hematuria 時間: 2025-3-29 10:37
Depthwise Separable Convolutions and Variational Dropout within the context of YOLOv3n solutions. However, these algorithms often impose prohibitive levels of memory and computational overhead, especially in resource-constrained environments. In this study, we combine the state-of-the-art object-detection model YOLOv3 with depthwise separable convolutions and variational dropout in 作者: 割讓 時間: 2025-3-29 13:58
Uncertainty Estimates in Deep Generative Models Using Gaussian Processesliability of the outcome of machine learning systems. Gaussian processes are widely known as a method in machine learning which provides estimates of uncertainty. Moreover, Gaussian processes have been shown to be equivalent to deep neural networks with infinitely wide layers. This equivalence sugge作者: 哭得清醒了 時間: 2025-3-29 17:54
Towards Optimal Ship Navigation Using Image Processing Plotting Aid (ARPA) and Electronic Chart Display and Information System (ECDIS). Location map, marine traffic, geographical conditions, and obstacles in a region can be monitored by these technologies. The obstacles may vary from icebergs and ice blocks to islands, debris, rocks, or other vessels i作者: chalice 時間: 2025-3-29 20:09 作者: 散步 時間: 2025-3-30 03:51
Pixel-Level Corrosion Detection on Metal Constructions by Fusion of Deep Learning Semantic and Conto approaches tend to place bounding boxes around the defected region which is not adequate both for structural analysis and prefabrication, an innovative construction concept which reduces maintenance cost, time and improves safety. In this paper, we apply three semantic segmentation-oriented deep le作者: allude 時間: 2025-3-30 05:40 作者: Suggestions 時間: 2025-3-30 10:32 作者: OREX 時間: 2025-3-30 15:27 作者: Thyroid-Gland 時間: 2025-3-30 19:57
https://doi.org/10.1007/978-3-030-64556-4artificial intelligence; computer vision; hci; human-computer interaction; image analysis; image coding; i作者: poliosis 時間: 2025-3-31 00:20 作者: 考得 時間: 2025-3-31 02:22 作者: 下級 時間: 2025-3-31 08:32 作者: 可耕種 時間: 2025-3-31 10:26 作者: Hdl348 時間: 2025-3-31 14:14
Conservation of Architectural Heritagedels across these two types of images, where human perceptual scores and optical character recognition accuracy are the respective quality metrics. In this paper we propose a novel contractive generative adversarial model to learn a unified quality-aware representation of images from heterogeneous s作者: 不愿 時間: 2025-3-31 20:26
