作者: escalate 時間: 2025-3-21 23:02 作者: indignant 時間: 2025-3-22 02:31
Domain Adaptation with a Domain Specific Class Means Classifierource domains. We make two contributions to the domain adaptation problem. First we extend the Nearest Class Mean (NCM) classifier by introducing for each class domain-dependent mean parameters as well as domain-specific weights. Second, we propose a generic adaptive semi-supervised metric learning 作者: OMIT 時間: 2025-3-22 04:38
Nonlinear Cross-View Sample Enrichment for Action Recognitionlts from the high expense to label training samples and their insufficiency to capture enough variability due to viewpoint changes..In this paper, we propose a solution that enriches training data by transferring their features across views. The proposed method is motivated by the fact that cross-vi作者: 警告 時間: 2025-3-22 12:12
Multi-Modal Distance Metric Learning: ABayesian Non-parametric Approachgeneous sources. Learning a similarity measure for such data is of great importance for vast number of applications such as ., ., ., etc..Defining an appropriate distance metric between data points with multiple modalities is a key challenge that has a great impact on the performance of many multime作者: 粘連 時間: 2025-3-22 12:55
Multi-Task Multi-Sample Learningle positive sample and all negative samples for the class. In this paper we develop a .?(MSL) model which enables joint regularization of the E-SVMs without any additional cost over the original ensemble learning. The advantage of the MSL model is that the degree of sharing between positive samples 作者: 粘連 時間: 2025-3-22 19:25
Learning Action Primitives for Multi-level Video Event Understandingfound recognition algorithms. In order to address this, we present an approach to discover action primitives, sub-categories of action classes, that allow us to model this intra-class variation. We learn action primitives and their interrelations in a multi-level spatio-temporal model for action rec作者: 食品室 時間: 2025-3-23 00:05
Learning Skeleton Stream Patterns with Slow Feature Analysis for Action RecognitionED lights). The motion sequences are collected into MoCap action datasets, e.g., 1973 [.] and CMU [.] MoCap action datasets.) action data suggest that skeleton joint streams contain sufficient intrinsic information for understanding human body actions. With the advancement in depth sensors, e.g., Ki作者: 設(shè)想 時間: 2025-3-23 02:43
A Novel Visual Word Co-occurrence Model for Person Re-identificationem is fundamentally challenging due to appearance variations resulting from differing poses, illumination and configurations of camera views. To deal with these difficulties, we propose a novel visual word co-occurrence model. We first map each pixel of an image to a visual word using a codebook, wh作者: initiate 時間: 2025-3-23 06:47
Joint Learning for Attribute-Consistent Person Re-Identificationmatching people across cameras with different viewpoints and lighting conditions, as well as across human pose variations. The literature has since devised several approaches to tackle these challenges, but the vast majority of the work has been concerned with appearance-based methods. We propose an作者: eucalyptus 時間: 2025-3-23 13:19 作者: Minutes 時間: 2025-3-23 17:55 作者: 離開真充足 時間: 2025-3-23 18:18 作者: 鬼魂 時間: 2025-3-23 23:04 作者: 顯微鏡 時間: 2025-3-24 03:09
Regularized Bayesian Metric Learning for Person Re-identification surveillance. In this paper, we propose a new regularized Bayesian metric learning (RBML) method for person re-identification. While numerous metric learning methods have been proposed for person re-identification in recent years, most of them suffer from the small sample size (SSS) problem because作者: liaison 時間: 2025-3-24 08:41
Investigating Open-World Person Re-identification Using a Droneical role in underpinning many multi-camera surveillance tasks. A fundamental assumption in almost all existing re-identification research is that cameras are in fixed emplacements, allowing the explicit modelling of camera and inter-camera properties in order to improve re-identification. In this p作者: TIGER 時間: 2025-3-24 13:52 作者: 準(zhǔn)則 時間: 2025-3-24 16:18
Conference proceedings 2015erception of affordance and functional visual primitives for scene analysis; graphical models in computer vision; light fields for computer vision; computer vision for road scene understanding and autonomous driving; soft biometrics; transferring and adapting source knowledge in computer vision; sur作者: 法律的瑕疵 時間: 2025-3-24 22:11
The Current Status of Cardiac Surgerythe flexible Beta process model, we can infer the dimensionality of the hidden space using training data itself. We also develop a novel Variational Bayes (VB) algorithm to compute the posterior distribution of the parameters that imposes the constraints (similarity/dissimilarity constraints) direct作者: 輕推 時間: 2025-3-25 00:43 作者: inspired 時間: 2025-3-25 04:56 作者: Encapsulate 時間: 2025-3-25 10:23
https://doi.org/10.1057/9781137464354ystem without having to implement a Pedestrian Detector algorithm yourself. We also provide body-part detection data on top of the manually labeled data and the Pedestrian Detection data, such as to make it trivial to extract your features from relevant local regions (actual body-parts). Finally we 作者: Calibrate 時間: 2025-3-25 13:37 作者: FRONT 時間: 2025-3-25 17:55
Multi-Modal Distance Metric Learning: ABayesian Non-parametric Approachthe flexible Beta process model, we can infer the dimensionality of the hidden space using training data itself. We also develop a novel Variational Bayes (VB) algorithm to compute the posterior distribution of the parameters that imposes the constraints (similarity/dissimilarity constraints) direct作者: fiction 時間: 2025-3-25 23:52 作者: 上釉彩 時間: 2025-3-26 03:27
Learning Skeleton Stream Patterns with Slow Feature Analysis for Action Recognitionms. Then, Slow Feature Analysis is applied to learn the visual pattern of each joint, and the high-level information in the learnt general patterns is encoded into each skeleton to reduce the intra-variance of the skeletons. Temporal pyramid of posture word histograms is used to describe the global 作者: 修飾 時間: 2025-3-26 04:19
The HDA+ Data Set for Research on Fully Automated Re-identification Systemsystem without having to implement a Pedestrian Detector algorithm yourself. We also provide body-part detection data on top of the manually labeled data and the Pedestrian Detection data, such as to make it trivial to extract your features from relevant local regions (actual body-parts). Finally we 作者: SUGAR 時間: 2025-3-26 10:34
Computer Vision - ECCV 2014 Workshops978-3-319-16199-0Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Plaque 時間: 2025-3-26 13:34 作者: Morose 時間: 2025-3-26 16:51
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/234010.jpg作者: 尖牙 時間: 2025-3-26 23:25 作者: 能量守恒 時間: 2025-3-27 01:57 作者: 制定法律 時間: 2025-3-27 05:38 作者: Nonporous 時間: 2025-3-27 12:46
Transplantation of other organsses, the visual community misses a large scale testbed for cross-dataset analysis. In this paper we discuss the challenges faced when aligning twelve existing image databases in a unique corpus, and we propose two cross-dataset setups that introduce new interesting research questions. Moreover, we r作者: perjury 時間: 2025-3-27 16:11 作者: 衰老 時間: 2025-3-27 21:12 作者: 東西 時間: 2025-3-28 01:27 作者: abolish 時間: 2025-3-28 04:07
Gong-Ru Lin,Yu-Chuan Su,Yu-Chieh Chile positive sample and all negative samples for the class. In this paper we develop a .?(MSL) model which enables joint regularization of the E-SVMs without any additional cost over the original ensemble learning. The advantage of the MSL model is that the degree of sharing between positive samples 作者: diabetes 時間: 2025-3-28 08:18
The Current Trends of Optics and Photonicsfound recognition algorithms. In order to address this, we present an approach to discover action primitives, sub-categories of action classes, that allow us to model this intra-class variation. We learn action primitives and their interrelations in a multi-level spatio-temporal model for action rec作者: Schlemms-Canal 時間: 2025-3-28 12:09
Gong-Ru Lin,Yu-Chuan Su,Yu-Chieh ChiED lights). The motion sequences are collected into MoCap action datasets, e.g., 1973 [.] and CMU [.] MoCap action datasets.) action data suggest that skeleton joint streams contain sufficient intrinsic information for understanding human body actions. With the advancement in depth sensors, e.g., Ki作者: 發(fā)微光 時間: 2025-3-28 14:36 作者: Cardioversion 時間: 2025-3-28 22:12
Gong-Ru Lin,Yu-Chuan Su,Yu-Chieh Chimatching people across cameras with different viewpoints and lighting conditions, as well as across human pose variations. The literature has since devised several approaches to tackle these challenges, but the vast majority of the work has been concerned with appearance-based methods. We propose an作者: Defiance 時間: 2025-3-28 23:33
https://doi.org/10.1007/978-94-017-9392-6s of the discriminating power of their characteristic features. In our approach, we first segment the pedestrian images into meaningful parts, then we extract features from such parts as well as from the whole body and finally, we perform a salience analysis based on regression coefficients. Given a作者: 天然熱噴泉 時間: 2025-3-29 05:10 作者: 反感 時間: 2025-3-29 09:05
Ann Marie Ryan,Charles Tocci,Seungho Moones multiple local single target trackers to hypothesise short term tracks. These tracks are combined with the tracks obtained by a global multi-target tracker, if they result in a reduction in the global cost function. Since tracking failures typically arise when targets become occluded, we propose 作者: WITH 時間: 2025-3-29 14:06 作者: escalate 時間: 2025-3-29 16:18
Natural Resources and Sustainability, surveillance. In this paper, we propose a new regularized Bayesian metric learning (RBML) method for person re-identification. While numerous metric learning methods have been proposed for person re-identification in recent years, most of them suffer from the small sample size (SSS) problem because作者: 后退 時間: 2025-3-29 23:04
,What’s Your Innovation Process?,ical role in underpinning many multi-camera surveillance tasks. A fundamental assumption in almost all existing re-identification research is that cameras are in fixed emplacements, allowing the explicit modelling of camera and inter-camera properties in order to improve re-identification. In this p作者: CODA 時間: 2025-3-30 01:43 作者: ATP861 時間: 2025-3-30 05:38
Conference proceedings 2015njunction with the 13th European Conference on Computer Vision, ECCV 2014, held in Zurich, Switzerland, in September 2014..The 203 workshop papers were carefully reviewed and selected for inclusion in the proceedings. They where presented at workshops with the following themes: where computer vision作者: Notify 時間: 2025-3-30 09:37
Saliency Weighted Features for Person Re-identificationance between image pairs and to re-identify a person. The proposed method is evaluated on three different benchmark datasets and compared with best state-of-the-art approaches to show its overall superior performance.作者: d-limonene 時間: 2025-3-30 14:09
Theoretical and Empirical Background,ance between image pairs and to re-identify a person. The proposed method is evaluated on three different benchmark datasets and compared with best state-of-the-art approaches to show its overall superior performance.作者: 健壯 時間: 2025-3-30 18:53 作者: Eeg332 時間: 2025-3-30 22:45
The Current Trends of Optics and Photonicson & configuration change across camera views. Linear SVMs are then trained as classifiers using these co-occurrence descriptors. On the VIPeR [.] and CUHK Campus [.] benchmark datasets, our method achieves 83.86% and 85.49% at rank-15 on the Cumulative Match Characteristic (CMC) curves, and beats the state-of-the-art results by 10.44% and 22.27%.作者: HALL 時間: 2025-3-31 04:38
Gong-Ru Lin,Yu-Chuan Su,Yu-Chieh Chidel and demonstrate the performance gain yielded by coupling both tasks. Our results outperform several state-of-the-art methods on VIPeR, a standard re-identification dataset. Finally, we report similar results on a new large-scale dataset we collected and labeled for our task.作者: EWER 時間: 2025-3-31 08:00 作者: 容易生皺紋 時間: 2025-3-31 11:58
,What’s Your Innovation Process?, dataset for mobile re-identification, and we use this to elucidate the unique challenges of mobile re-identification. Finally, we re-evaluate some conventional wisdom about re-id models in the light of these challenges and suggest future avenues for research in this area.作者: 消毒 時間: 2025-3-31 14:24
Nonlinear Cross-View Sample Enrichment for Action Recognition views by back-projecting their CCA features from latent to view-dependent spaces..We experiment this cross-view sample enrichment process for action classification and we study the impact of several factors including kernel choices as well as the dimensionality of the latent spaces.作者: mastoid-bone 時間: 2025-3-31 19:08
A Novel Visual Word Co-occurrence Model for Person Re-identificationon & configuration change across camera views. Linear SVMs are then trained as classifiers using these co-occurrence descriptors. On the VIPeR [.] and CUHK Campus [.] benchmark datasets, our method achieves 83.86% and 85.49% at rank-15 on the Cumulative Match Characteristic (CMC) curves, and beats the state-of-the-art results by 10.44% and 22.27%.作者: 隱士 時間: 2025-3-31 22:16
Joint Learning for Attribute-Consistent Person Re-Identificationdel and demonstrate the performance gain yielded by coupling both tasks. Our results outperform several state-of-the-art methods on VIPeR, a standard re-identification dataset. Finally, we report similar results on a new large-scale dataset we collected and labeled for our task.作者: 鉗子 時間: 2025-4-1 02:18
