標(biāo)題: Titlebook: Advances in Multimedia Information Processing – PCM 2017; 18th Pacific-Rim Con Bing Zeng,Qingming Huang,Xiaopeng Fan Conference proceedings [打印本頁(yè)] 作者: Roosevelt 時(shí)間: 2025-3-21 18:27
書目名稱Advances in Multimedia Information Processing – PCM 2017影響因子(影響力)
書目名稱Advances in Multimedia Information Processing – PCM 2017影響因子(影響力)學(xué)科排名
書目名稱Advances in Multimedia Information Processing – PCM 2017網(wǎng)絡(luò)公開度
書目名稱Advances in Multimedia Information Processing – PCM 2017網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Advances in Multimedia Information Processing – PCM 2017被引頻次
書目名稱Advances in Multimedia Information Processing – PCM 2017被引頻次學(xué)科排名
書目名稱Advances in Multimedia Information Processing – PCM 2017年度引用
書目名稱Advances in Multimedia Information Processing – PCM 2017年度引用學(xué)科排名
書目名稱Advances in Multimedia Information Processing – PCM 2017讀者反饋
書目名稱Advances in Multimedia Information Processing – PCM 2017讀者反饋學(xué)科排名
作者: Initial 時(shí)間: 2025-3-21 21:00
Multiple Kernel Learning Based on Weak Learner for Automatic Image Annotationmance, we combine the boosting procedure with the multiple kernel learning to enhance the performance of classifier. We evaluate the proposed method on two benchmark datasets. The experimental results demonstrate that our method is superior to several state-of-the-art methods.作者: Amorous 時(shí)間: 2025-3-22 04:26 作者: 案發(fā)地點(diǎn) 時(shí)間: 2025-3-22 04:35 作者: 抵消 時(shí)間: 2025-3-22 12:31
Conference proceedings 2018dia; 3D and Panoramic Vision; Deep Learning for Signal Processing and Understanding; Large-Scale Multimedia Affective Computing; Sensor-enhanced Multimedia Systems; Content Analysis; Coding, Compression, Transmission, and Processing..作者: exercise 時(shí)間: 2025-3-22 13:48 作者: Acupressure 時(shí)間: 2025-3-22 20:16
Marta Peris-Ortiz,Jo?o J. Ferreiraugh our proposed strategy out-performs the Game AI rarely as we did not account game playing-speed that makes a huge difference in victory but at least we succeeded in introducing a strategy that could well compete the Game AI and may defeat it but rarely.作者: Estimable 時(shí)間: 2025-3-22 21:15 作者: 傲慢物 時(shí)間: 2025-3-23 01:49 作者: Bph773 時(shí)間: 2025-3-23 09:17 作者: 極大痛苦 時(shí)間: 2025-3-23 13:14
An Introduction to Cooperativesxperimental results demonstrate that our method significantly outperform previous baseline SCRC (Spatial Context Recurrent ConvNet) [.] model on Referit dataset [.], moreover, our model is simple to train similar to Faster R-CNN.作者: 侵略主義 時(shí)間: 2025-3-23 17:54 作者: Negotiate 時(shí)間: 2025-3-23 21:11 作者: 特征 時(shí)間: 2025-3-24 01:43 作者: 休息 時(shí)間: 2025-3-24 02:20 作者: podiatrist 時(shí)間: 2025-3-24 07:10
An Efficient Feature Selection for SAR Target Classificationnt features. Finally, for target classification, SVM is used as a baseline classifier. Experiments on MSTAR public release dataset are conducted, and the results demonstrate that the proposed method outperforms the state-of-the-art methods.作者: cunning 時(shí)間: 2025-3-24 11:44
Automatic Foreground Seeds Discovery for Robust Video Saliency Detectionobal object appearance model using the initial seeds and remove unreliable seeds according to foreground likelihood. Finally, the seeds work as queries to rank all the superpixels in images to generate saliency maps. Experimental results on challenging public dataset demonstrate the advantage of our algorithm over state-of-the-art algorithms.作者: FID 時(shí)間: 2025-3-24 18:34 作者: intercede 時(shí)間: 2025-3-24 20:05
Object Discovery and Cosegmentation Based on Dense Correspondencessides, due to the powerful feature learning ability of deep models, we adopt VGG features to do unsupervised clustering and find representative candidates as a prior knowledge. Experiments on noisy datasets show the effectiveness of our method.作者: 服從 時(shí)間: 2025-3-24 23:38
Fusing Appearance Features and Correlation Features for Face Video Retrievalnd hash learning into a unified optimization framework to guarantee optimal compatibility of appearance features and correlation features. Experiments on two challenging TV-Series datasets demonstrate the effectiveness of the proposed method.作者: lactic 時(shí)間: 2025-3-25 06:50 作者: GIDDY 時(shí)間: 2025-3-25 08:55
Julio C. Gambina,Gabriela Roffinellih stroke information, which has never been considered in the task of fine-art painting classification. Experiments demonstrate that the proposed model achieves better classification performance than other models. Moreover, each stage of our model is effective for the image classification.作者: myopia 時(shí)間: 2025-3-25 12:53
Luiz Inácio Gaiger,Eliene Dos Anjoset an appropriate answer. In particular, in this STCN framework, we effectively fuse optical flow to capture more discriminative motion information of videos. In order to verify the effectiveness of the proposed framework, we conduct experiments on TACoS dataset. It achieves good performances on both hard level and easy level of TACoS dataset.作者: Medicare 時(shí)間: 2025-3-25 19:10 作者: 咽下 時(shí)間: 2025-3-25 21:37
Introduction to Steady-State Systems novel framework for action recognition, which combines 2D ConvNets and 3D ConvNets. The accuracy of MMFN outperforms the state-of-the-art deep-learning-based methods on the datasets of UCF101 (94.6%) and HMDB51 (69.7%).作者: Kidney-Failure 時(shí)間: 2025-3-26 03:22
Multi-modality Fusion Network for Action Recognition novel framework for action recognition, which combines 2D ConvNets and 3D ConvNets. The accuracy of MMFN outperforms the state-of-the-art deep-learning-based methods on the datasets of UCF101 (94.6%) and HMDB51 (69.7%).作者: Cleave 時(shí)間: 2025-3-26 08:09 作者: Jejune 時(shí)間: 2025-3-26 10:09 作者: Demulcent 時(shí)間: 2025-3-26 12:56
Spatio-Temporal Context Networks for Video Question Answeringet an appropriate answer. In particular, in this STCN framework, we effectively fuse optical flow to capture more discriminative motion information of videos. In order to verify the effectiveness of the proposed framework, we conduct experiments on TACoS dataset. It achieves good performances on both hard level and easy level of TACoS dataset.作者: Arboreal 時(shí)間: 2025-3-26 20:43
https://doi.org/10.1007/978-3-319-44509-0RGB image, a representation encoding the predicted depth cue is generated. This predicted depth descriptors can be further fused with features from color channels. Experiments are performed on two indoor scene classification benchmarks and the quantitative comparisons demonstrate the effectiveness of proposed scheme.作者: Criteria 時(shí)間: 2025-3-27 00:14 作者: 吹牛者 時(shí)間: 2025-3-27 01:55
Introduction to Steady-State Systemsal prior of random walk model by adding an extra item into the weight matrix of the graph constructed from an image. Experimental results show that the proposed method acts better than Dense CRF in pixel accuracy and mean IoU, and obtains smoother results. In addition, our method significantly reduces the time cost of refinement process.作者: 機(jī)構(gòu) 時(shí)間: 2025-3-27 08:43
Cooperativity Theory in Biochemistrynce image using the dense motion fields of background and reflection respectively. Finally, with the initial solution provided, the background and reflection images can be separated using alternating optimization. Experiment results showed that our method can achieve a robust performance compared with the state of art.作者: 密切關(guān)系 時(shí)間: 2025-3-27 09:27
Cooperativity Theory in Biochemistrycale local appearance based keypoint likelihood with filtered viewpoint conditioned likelihood to induce a considerable performance gain. Experimentally, we show that our framework outperforms state-of-the-art methods on PASCAL 3D benchmark.作者: 小臼 時(shí)間: 2025-3-27 17:24
https://doi.org/10.1007/978-1-4757-3302-0rallelism method to accelerate constraint resolving and collision detection. As a result, our system can provide realistic effects for the virtual fitting while meeting the real-time and robustness requirements.作者: intricacy 時(shí)間: 2025-3-27 20:06 作者: fibula 時(shí)間: 2025-3-28 01:30 作者: 發(fā)誓放棄 時(shí)間: 2025-3-28 02:37 作者: Surgeon 時(shí)間: 2025-3-28 07:06 作者: Disk199 時(shí)間: 2025-3-28 12:26
A Fine-Grained Filtered Viewpoint Informed Keypoint Prediction from 2D Imagescale local appearance based keypoint likelihood with filtered viewpoint conditioned likelihood to induce a considerable performance gain. Experimentally, we show that our framework outperforms state-of-the-art methods on PASCAL 3D benchmark.作者: 無(wú)動(dòng)于衷 時(shí)間: 2025-3-28 16:44
More Efficient, Adaptive and Stable, A?Virtual Fitting System Using Kinectrallelism method to accelerate constraint resolving and collision detection. As a result, our system can provide realistic effects for the virtual fitting while meeting the real-time and robustness requirements.作者: CRP743 時(shí)間: 2025-3-28 20:31 作者: Germinate 時(shí)間: 2025-3-29 01:06 作者: 瘋狂 時(shí)間: 2025-3-29 03:42
Cooperative Differential Games,ime surveillance. This paper presents an effective method based on fully convolutional network (FCN), density-based spatial clustering of applications with noise (DBSCAN) and non-maximum suppression (NMS) algorithm. Our proposed approach captures the thermal face features automatically using FCN. Th作者: 倫理學(xué) 時(shí)間: 2025-3-29 08:30
,Non—Cooperative Differential Games,t proposal method on RGB-D images with the constraint of depth connectivity, which can improve the key techniques in grouping based object proposal effectively, including segment generation, hypothesis expansion and candidate ranking. Given an RGB-D image, we first generate segments using depth awar作者: 傾聽 時(shí)間: 2025-3-29 14:08
Masatoshi Sakawa,Ichiro Nishizakio roughly locate the salient object, which is combined with the color and texture to construct the feature space. Based on the feature space and fast background connection, a novel graph is put forward to effectively obtain the local and global cues and ease the blurry surrounds of the saliency maps作者: 施舍 時(shí)間: 2025-3-29 18:09 作者: Conflagration 時(shí)間: 2025-3-29 22:33
Misa Aoki,Taiki Kagami,Takashi Sugimoto of BoVW, we address this issue by proposing an efficient feature selection method for SAR target classification. First, Graphic Histogram of oriented Gradients (HOG) based features is adopted to extract features from the training SAR images. Second, a discriminative codebook is generated using K-me作者: octogenarian 時(shí)間: 2025-3-30 01:04
Julio C. Gambina,Gabriela Roffinelliannel deep residual network to classify fine-art painting images. In detail, we take the advantage of the ImageNet to pre-train the deep residual network. Our two channels include the RGB channel and the brush stroke information channel. The gray-level co-occurrence matrix is used to detect the brus作者: Original 時(shí)間: 2025-3-30 04:12 作者: 善于 時(shí)間: 2025-3-30 08:50
An Introduction to Cooperativesption of the target. The method, called semantic R-CNN, extends RPN (Region Proposal Network) [.] by adding LSTM [.] module for processing natural language query text. LSTM [.] module take encoded query text and image descriptors as input and output the probability of the query text conditioned on v作者: Evacuate 時(shí)間: 2025-3-30 12:24 作者: alabaster 時(shí)間: 2025-3-30 19:06
Luiz Inácio Gaiger,Eliene Dos Anjospposes that common object patterns are sparse concerning transformations across images. The key issue is then how to take advantage of the interrelations among images. Since an image normally matches better with similar images containing the same object than noise images, we exploit the image matchi作者: 不適 時(shí)間: 2025-3-30 22:08
Introduction to Steady-State Systems with conditional random fields (CRFs), however, cause significant increase in model complexity and scattered distribution of pixels in border regions. To address these issues, we propose a novel approach combining random walk with FCNs to capture global features and refine border regions of segment作者: 神秘 時(shí)間: 2025-3-31 04:29 作者: 一個(gè)姐姐 時(shí)間: 2025-3-31 06:28
Introduction to Steady-State Systemson features, which could degrade retrieval performance. In this paper, we fuse appearance features and correlation features to exploit rich information of face videos for face video retrieval via a deep convolutional neural network. The network extracts appearance feature and correlation feature fro作者: infelicitous 時(shí)間: 2025-3-31 09:42 作者: Fortify 時(shí)間: 2025-3-31 15:15
Cooperativity Theory in Biochemistryer, the cases of ambiguous viewpoint predicted by the convolutional neural network, especially for two peaks of high confidence viewpoint proposals, may specify a set of erroneous keypoints. To address the above issue, we present multiscale convolutional neural networks and propose a filter to ensur作者: 最高峰 時(shí)間: 2025-3-31 18:14 作者: Generic-Drug 時(shí)間: 2025-3-31 23:16
https://doi.org/10.1007/978-3-319-77383-4artificial intelligence; classification; computer vision; cryptography; data security; estimation; face re作者: 植物學(xué) 時(shí)間: 2025-4-1 04:24 作者: inscribe 時(shí)間: 2025-4-1 09:30 作者: 搬運(yùn)工 時(shí)間: 2025-4-1 11:40 作者: micturition 時(shí)間: 2025-4-1 14:20
A Competitive Combat Strategy and Tactics in RTS Games AI and StarCraftcreating an army and If he is building up his army, he is losing out on having a strong base. The key to winning, in StarCraft or any other RTS game is to balance strategy, tactics, macro and micro. To improve the game, one has to be able to keep track of everything that’s going on over the entire m作者: etiquette 時(shí)間: 2025-4-1 22:28
Indoor Scene Classification by?Incorporating Predicted Depth Descriptorh information within the image scene classification systems, mainly because the lack of depth labeling in existing monocular image datasets. In this paper, we introduce a framework to overcome this limitation by incorporating the predicted depth descriptor of the monocular images for indoor scene cl作者: 悅耳 時(shí)間: 2025-4-2 01:21
Multiple Thermal Face Detection in Unconstrained Environments Using Fully Convolutional Networksime surveillance. This paper presents an effective method based on fully convolutional network (FCN), density-based spatial clustering of applications with noise (DBSCAN) and non-maximum suppression (NMS) algorithm. Our proposed approach captures the thermal face features automatically using FCN. Th作者: 乏味 時(shí)間: 2025-4-2 05:15
Object Proposal via Depth Connectivity Constrained Groupingt proposal method on RGB-D images with the constraint of depth connectivity, which can improve the key techniques in grouping based object proposal effectively, including segment generation, hypothesis expansion and candidate ranking. Given an RGB-D image, we first generate segments using depth awar
