標題: Titlebook: Neural Information Processing; 26th International C Tom Gedeon,Kok Wai Wong,Minho Lee Conference proceedings 2019 Springer Nature Switzerla [打印本頁] 作者: 誤解 時間: 2025-3-21 16:30
書目名稱Neural Information Processing影響因子(影響力)
書目名稱Neural Information Processing影響因子(影響力)學科排名
書目名稱Neural Information Processing網(wǎng)絡公開度
書目名稱Neural Information Processing網(wǎng)絡公開度學科排名
書目名稱Neural Information Processing被引頻次
書目名稱Neural Information Processing被引頻次學科排名
書目名稱Neural Information Processing年度引用
書目名稱Neural Information Processing年度引用學科排名
書目名稱Neural Information Processing讀者反饋
書目名稱Neural Information Processing讀者反饋學科排名
作者: incredulity 時間: 2025-3-21 21:50
Multi-person 3D Pose Estimation from Monocular Image Sequencessidering the temporal smoothness. We evaluate our framework on the Human3.6M dataset and the multi-person image sequence captured by ourselves. The quantitative results on the Human3.6M dataset and the qualitative results on our constructed test data demonstrate the effectiveness of our proposed method.作者: Neutropenia 時間: 2025-3-22 01:55
Shape Description and Retrieval in a Fused Scale Spacee are extracted across scales. Finally, shape retrieval is conducted by an integration of the retrieval results individually yielded at multiple scales. Experimental results on benchmark datasets validate the accuracy, efficiency and robustness of our proposed method.作者: 杠桿支點 時間: 2025-3-22 06:02
Gated Contiguous Memory U-Net for Single Image Dehazingcombine the features of different levels. We evaluate our proposed method using two public image dehazing benchmarks. The experiments demonstrate that our network can achieve a state-of-the-art performance when compared with other popular methods.作者: 公司 時間: 2025-3-22 12:44
Conference proceedings 2019 different domains.?The second volume, LNCS 11954, is organized in topical sections on image processing by neural techniques; learning from incomplete data; model compression and optimisation; neural learning models; neural network applications; and social network computing..作者: barium-study 時間: 2025-3-22 13:41
Conference proceedings 2019g, ICONIP 2019, held in Sydney, Australia, in December 2019..The 173 full papers presented were carefully reviewed and selected from 645 submissions. The papers address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques across作者: 樹木中 時間: 2025-3-22 18:12
0302-9743 Processing, ICONIP 2019, held in Sydney, Australia, in December 2019..The 173 full papers presented were carefully reviewed and selected from 645 submissions. The papers address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniq作者: 向下 時間: 2025-3-23 01:01 作者: Middle-Ear 時間: 2025-3-23 04:46
STNet: A Style Transformation Network for Deep Image Steganographyarbitrary size with 0.06 bit per pixel, improving over other deep steganographic models which only can embed fixed-length secret. Experiment results demonstrate that our STNet can achieve great visual effect, security, and reliability.作者: FRAX-tool 時間: 2025-3-23 08:52 作者: 山羊 時間: 2025-3-23 13:34
A Robustness and Low Bit-Rate Image Compression Network for Underwater Acoustic Communicationn our training process. Extensive experiments show that this training method improves the robustness of decoder and reconstruction performance. Besides, the algorithm is better than common compression algorithms and DNN based algorithms for underwater acoustic communication.作者: 記憶 時間: 2025-3-23 16:11
RBPNET: An Asymptotic Residual Back-Projection Network for Super Resolution of Very Low Resolution Fexplicitly reconstruct the edge map of face image and embed it into the reconstruction of high-resolution face image to reduce distortion of super-resolution results. Extensive experiments demonstrate the effectiveness and advantages of our proposed RBPNet qualitatively and quantitatively.作者: 排他 時間: 2025-3-23 21:45 作者: 勉強 時間: 2025-3-24 02:02 作者: obstruct 時間: 2025-3-24 05:26 作者: cognizant 時間: 2025-3-24 08:15
Zihan Wang,Neng Gao,Xin Wang,Ji Xiang,Guanqun Liuntation, the possibility to reason about both the states and the operations, a well-defined semantics allowing formal analysis, and the possibility to make simulation or to generate code. However, the expressive power of Petri nets makes difficult the precise modelling of large systems, because of t作者: Firefly 時間: 2025-3-24 12:25 作者: 不感興趣 時間: 2025-3-24 17:20
Yingjie Xu,Wenan Zhou,Ying Xingd to the description of hardware components at various levels of abstraction. This language has been standardised by the IEEE under the names . and . [5,6]; the VHDL’87 version is considered here. VHDL and another standardised language Verilog [7,9] are used widely by the community of hardware desig作者: 驚惶 時間: 2025-3-24 20:07 作者: 共棲 時間: 2025-3-25 01:04
Yu Sang,Jinguang Sun,Simiao Wang,Yanfei Peng,Xinjun Zhang,Zhiyang Yangresentations of digital signal processing algorithms has been given, and based on this theory, techniques have been presented for compiling SDF programs into efficient code for programmable processors. The techniques have focused on the minimization of code size, and the minimization of the buffer m作者: Physiatrist 時間: 2025-3-25 06:00
Sheng He,Genke Yangs synchronous dataflow graphs for programmabledigital signal processors (DSPs) used in embedded real- time systems.The advent of high-speed graphics workstations has made feasible theuse of graphical block diagram programming environments by designersof signal processing systems. A particular subset作者: IOTA 時間: 2025-3-25 10:42 作者: 無法解釋 時間: 2025-3-25 14:35
Songyi Gao,Weijie Shen,Zelin Liu,An Zhu,Yang Yunent of structured programming, which relies on simplistic cognitive models of top-down planning and comprehension, does not include many of the phenomena revealed by recent research. The ability to decompose problems into satisfactory hierarchic strctures apparently requires a learent repertoire of作者: ALLEY 時間: 2025-3-25 16:07 作者: MURAL 時間: 2025-3-25 20:42
Mingyong Zhuang,Yan Luo,Xinghao Ding,Yue Huang,Yinghao Liaoox in nature. The structure of these models reflect supposed scientific observation of the physical processes of software failure, together with the conventional desire for modelling brevity. It is, unfortunately, the case that such models, if automatically applied, may neglect important but unknown作者: d-limonene 時間: 2025-3-26 00:18
Lei Xiang,Hang Dong,Fei Wang,Yu Guo,Kaisheng Manent of structured programming, which relies on simplistic cognitive models of top-down planning and comprehension, does not include many of the phenomena revealed by recent research. The ability to decompose problems into satisfactory hierarchic strctures apparently requires a learent repertoire of作者: 蘑菇 時間: 2025-3-26 05:39
Shugang Cui,Shu Tian,Xucheng Yinox in nature. The structure of these models reflect supposed scientific observation of the physical processes of software failure, together with the conventional desire for modelling brevity. It is, unfortunately, the case that such models, if automatically applied, may neglect important but unknown作者: Distribution 時間: 2025-3-26 10:33 作者: 愉快么 時間: 2025-3-26 13:28 作者: 撫慰 時間: 2025-3-26 17:19
Wenjian Liu,Yue Zhounical structural problems. The results are accepted or rejected by means of comparison of state variables (stresses, displacements, natural frequencies etc.) and user requirements. In further analyses the design variables will be updated until the user specifications are met and the design is feasib作者: Indelible 時間: 2025-3-26 22:09 作者: 該得 時間: 2025-3-27 03:21 作者: 現(xiàn)任者 時間: 2025-3-27 09:06 作者: faddish 時間: 2025-3-27 12:44 作者: Sciatica 時間: 2025-3-27 15:24 作者: Control-Group 時間: 2025-3-27 18:04
Image Denoising Networks with Residual Blocks and RReLUs application range is greatly restricted by the specialized task (i.e., a specific model is required for each considered noise level), which prompts us to train a single network to tackle the blind image denoising problem. To this end, we take the advantages of state-of-the-art progress in deep lear作者: 發(fā)微光 時間: 2025-3-28 01:52
Shape Description and Retrieval in a Fused Scale Spaception. First, morphological operations and the Gaussian smoothing are jointly used to produce a fused scale-space description of the input shape, which is able to handle strong noise, intra-class shape variation and irregular deformation simultaneously. Then, the height-function features of the shap作者: 火車車輪 時間: 2025-3-28 05:50
Only Image Cosine Embedding for Few-Shot Learningning, a method of learning to learn, is introduced into few-shot learning problem and has achieved pretty good results. But there is still a very big gap between the machine and our human in the few-shot learning tasks. We think it’s because the existing methods do not make full use of global knowle作者: mercenary 時間: 2025-3-28 06:16
Deep 3D Segmentation and Classification of Point Clouds for Identifying AusRAP Attributesement. The major challenges are accurately segmenting and classifying AusRAP attributes. Researchers have focused on sematic segmentation and object classification to address the challenges mostly in 2D image setting, and few of them have recently extended techniques from 2D to 3D setting. However, 作者: ANTE 時間: 2025-3-28 12:13 作者: Frequency 時間: 2025-3-28 17:50
Gated Contiguous Memory U-Net for Single Image Dehazinget like deep network with contiguous memory residual blocks and gated fusion sub-network module to deal with the single image dehazing problem. The contiguous memory residual block is used to increase the flow of information by feature reusing and a gated fusion sub-network module is used to better 作者: 搜集 時間: 2025-3-28 19:56
Combined Correlation Filters with Siamese Region Proposal Network for Visual Trackingal Network (SiamRPN) tracker can get more accurate bounding box with proposal refinement, yet, most siamese trackers are lack of discrimination without target classification and robustness without online learning module. To tackle the problem, in this paper, we propose an ensemble tracking framework作者: custody 時間: 2025-3-29 02:14
RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instrumentsruments is still a challenge due to specular reflection and class imbalance issues. In this paper, an attention-guided network is proposed to segment the cataract surgical instrument. A new attention module is designed to learn discriminative features and address the specular reflection issue. It ca作者: jagged 時間: 2025-3-29 03:53
A Novel Image-Based Malware Classification Model Using Deep Learningthis paper, we propose a novel image-based malware classification model using deep learning to counter large-scale malware analysis. The model includes a malware embedding method called YongImage which maps instruction-level information and disassembly metadata generated by IDA disassembler tool int作者: 開始沒有 時間: 2025-3-29 09:53
Visual Saliency Detection via Convolutional Gated Recurrent Units frameworks is still an open problem. Recent saliency detection models designed using complex Deep Neural Networks to extract robust features, however often fail to select the right contextual features. These methods generally utilize physical attributes of objects for generating final saliency maps作者: 較早 時間: 2025-3-29 13:33 作者: 調色板 時間: 2025-3-29 19:08 作者: delusion 時間: 2025-3-29 22:08
Reinforcing LiDAR-Based 3D Object Detection with RGB and 3D Informationation and 3D boxes regression. However, some background and foreground objects may have similar appearances in point clouds. Therefore the accuracy of LiDAR-based 3D object detection is hard to be improved. In this paper, we propose a three-stage 3D object detection method called RGB3D to reinforce 作者: 假 時間: 2025-3-29 23:59 作者: 刀鋒 時間: 2025-3-30 04:34 作者: 現(xiàn)存 時間: 2025-3-30 12:12
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/n/image/663608.jpg作者: Medicaid 時間: 2025-3-30 16:00
https://doi.org/10.1007/978-3-030-36711-4artificial intelligence; classification; cognitive neurosciences; computational linguistics; computation作者: 笨拙處理 時間: 2025-3-30 17:22
device is described by means of a set of equations, this style roughly corresponds to the “Register Transfer” level of abstraction), and the “structural” style (the device is described as a set of interconnected components). In this chapter, we consider only the behavioural style.作者: Evolve 時間: 2025-3-30 21:52 作者: Gratulate 時間: 2025-3-31 03:43
Zihan Wang,Neng Gao,Xin Wang,Ji Xiang,Guanqun Liuprovides efficient concepts to cope with their behaviour. Throughout this chapter, Petri net refers to High-Level Petri Net formalisms such as [3,6,8,9,1] allowing to handle tokens which may be identified, and more precisely to the . formalism [9,11].作者: exhilaration 時間: 2025-3-31 05:37
Ran Li,Nayun Xu,Xutong Lu,Yucheng Xing,Haohua Zhao,Li Niu,Liqing Zhangprovides efficient concepts to cope with their behaviour. Throughout this chapter, Petri net refers to High-Level Petri Net formalisms such as [3,6,8,9,1] allowing to handle tokens which may be identified, and more precisely to the . formalism [9,11].作者: 手術刀 時間: 2025-3-31 11:22 作者: instate 時間: 2025-3-31 15:35
Yongqiang Zhao,Yuan Rao,Shipeng Dong,Jiangnan Qiframework that guides the way in which organizations should properly govern and manage Green IT. To this end, we have developed the . (GMGIT), validating and refining it through different case studies at an international level, obtaining several versions through an iterative and incremental cycle. T作者: 情節(jié)劇 時間: 2025-3-31 19:57
Yu Sang,Jinguang Sun,Simiao Wang,Yanfei Peng,Xinjun Zhang,Zhiyang Yangle appearance schedules for acyclic graphs that minimize data memory size, and an extensive experimental evaluation of these techniques on practical and random graphs. These techniques can easily be incorporated into the scheduling framework described in Chapter 5. Despite the focus on single appear作者: remission 時間: 2025-3-31 23:02
Wen Zhou,Baojiang Zhong,Jianyu Yangphysical environment in which the system operates. Deterministic and stochastic state computer systems are defined and it is noted that the failure patterns for these systems are typically different. In addition, a method for assessing a software reliability parameter based on random sampling of the
