作者: 暫時(shí)過來 時(shí)間: 2025-3-21 22:02
Learning Action Concept Trees and Semantic Alignment Networks from Image-Description Dataequires tremendous manual work, which is hard to scale up. Besides, the action categories in such datasets are pre-defined and vocabularies are fixed. However humans may describe the same action with different phrases, which leads to the difficulty of vocabulary expansion for traditional fully-super作者: jabber 時(shí)間: 2025-3-22 02:32 作者: cluster 時(shí)間: 2025-3-22 06:21 作者: WITH 時(shí)間: 2025-3-22 10:07
Parametric Image Segmentation of Humans with Structural Shape Priorsgrounds, articulation, varying body proportions, partial views and viewpoint changes. In this work we propose class-specific segmentation models that leverage parametric max-flow image segmentation and a large dataset of human shapes. Our contributions are as follows: (1) formulation of a sub-modula作者: 不可救藥 時(shí)間: 2025-3-22 13:28
Lip Reading in the Wild trying to recognise a small number of utterances in controlled environments (. digits and alphabets), partially due to the shortage of suitable datasets..We make two novel contributions: first, we develop a pipeline for fully automated large-scale data collection from TV broadcasts. With this we ha作者: 不可救藥 時(shí)間: 2025-3-22 18:21 作者: 存心 時(shí)間: 2025-3-22 21:28
Continuous Supervised Descent Method for Facial Landmark Localisation to address this issue we propose a second order linear regression method that is both compact and robust against strong rotations. We provide a closed form solution, making the method fast to train. We test the method’s performance on two challenging datasets. The first has been intensely used by t作者: formula 時(shí)間: 2025-3-23 03:10
Modeling Stylized Character Expressions via Deep Learningcognize the expression of humans and stylized characters independently. Then we utilize a transfer learning technique to learn the mapping from humans to characters to create a shared embedding feature space. This embedding also allows human expression-based image retrieval and character expression-作者: 調(diào)色板 時(shí)間: 2025-3-23 07:58
Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Unitseling approach. In particular, we introduce GP . to project multiple observed features onto a latent space, while GP . are responsible for reconstructing the original features. Inference is performed in a novel variational framework, where the recovered latent representations are further constrained作者: committed 時(shí)間: 2025-3-23 13:36 作者: 神圣將軍 時(shí)間: 2025-3-23 16:11
Analysis on the Dropout Effect in Convolutional Neural NetworksIn convolutional neural networks (CNNs), dropout is usually applied to the fully connected layers. Meanwhile, the regularization effect of dropout in the convolutional layers has not been thoroughly analyzed in the literature. In this paper, we analyze the effect of dropout in the convolutional laye作者: noxious 時(shí)間: 2025-3-23 20:30 作者: 果仁 時(shí)間: 2025-3-24 01:36
Joint Training of Generic CNN-CRF Models with Stochastic Optimizationork (CNN) and Conditional Random Field (CRF) parameters. While stochastic gradient descent is a standard technique for CNN training, it was not used for joint models so far. We show that our learning method is (i) general, i.e.?it applies to arbitrary CNN and CRF architectures and potential function作者: chronicle 時(shí)間: 2025-3-24 03:50 作者: Rejuvenate 時(shí)間: 2025-3-24 07:10 作者: 身心疲憊 時(shí)間: 2025-3-24 11:36
https://doi.org/10.1057/9781137486431 that systematically computes all breakpoints of the model in polynomial time; (2) design of a data-driven class-specific fusion methodology, based on matching against a large training set of exemplar human shapes (100,000 in our experiments), that ..作者: 王得到 時(shí)間: 2025-3-24 16:23
https://doi.org/10.1007/978-3-319-02517-9ble to effectively learn and recognize hundreds of words from this large-scale dataset..We also demonstrate a recognition performance that exceeds the state of the art on a standard public benchmark dataset.作者: 白楊魚 時(shí)間: 2025-3-24 22:43
Safety Evaluation (Animal Studies),eval tasks on our collected stylized character dataset of expressions. We also show that the ranking order predicted by the proposed features is highly correlated with the ranking order provided by a facial expression expert and Mechanical Turk experiments.作者: 健談 時(shí)間: 2025-3-25 00:38 作者: cloture 時(shí)間: 2025-3-25 04:09
https://doi.org/10.1007/978-3-319-91418-3ed CNN-CRF optimization approach simplifies a potential hardware implementation. We empirically evaluate our method on the task of semantic labeling of body parts in depth images and show that it compares favorably to competing techniques.作者: CLAMP 時(shí)間: 2025-3-25 10:58
Parametric Image Segmentation of Humans with Structural Shape Priors that systematically computes all breakpoints of the model in polynomial time; (2) design of a data-driven class-specific fusion methodology, based on matching against a large training set of exemplar human shapes (100,000 in our experiments), that ..作者: olfction 時(shí)間: 2025-3-25 12:50
Lip Reading in the Wildble to effectively learn and recognize hundreds of words from this large-scale dataset..We also demonstrate a recognition performance that exceeds the state of the art on a standard public benchmark dataset.作者: 繁榮地區(qū) 時(shí)間: 2025-3-25 16:30
Modeling Stylized Character Expressions via Deep Learningeval tasks on our collected stylized character dataset of expressions. We also show that the ranking order predicted by the proposed features is highly correlated with the ranking order provided by a facial expression expert and Mechanical Turk experiments.作者: seroma 時(shí)間: 2025-3-25 20:12 作者: Alveoli 時(shí)間: 2025-3-26 01:45 作者: Diverticulitis 時(shí)間: 2025-3-26 07:27 作者: 光明正大 時(shí)間: 2025-3-26 12:14
Conference proceedings 2017ce and Gestures; Image Alignment; Computational Photography and Image Processing; Language and Video; 3D Computer Vision; Image Attributes, Language, and Recognition; Video Understanding; and 3D Vision..作者: 雪崩 時(shí)間: 2025-3-26 16:38
0302-9743 tions; Faces; Computational Photography; Face and Gestures; Image Alignment; Computational Photography and Image Processing; Language and Video; 3D Computer Vision; Image Attributes, Language, and Recognition; Video Understanding; and 3D Vision..978-3-319-54183-9978-3-319-54184-6Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: RENAL 時(shí)間: 2025-3-26 17:51 作者: judicial 時(shí)間: 2025-3-27 00:51
https://doi.org/10.1007/978-94-011-7701-6opose a method to learn an Action Concept Tree (ACT) and an Action Semantic Alignment (ASA) model for classification from image-description data via a two-stage learning process. A new dataset for the task of . is built. Experimental results show that our method outperforms several baseline methods significantly.作者: Mitigate 時(shí)間: 2025-3-27 03:23
Basic Scientific Characterisation,g RCPR, CGPRT, LBF, CFSS, and GSDM. Results upon both datasets show that the proposed method offers state-of-the-art performance on near frontal view data, improves state-of-the-art methods on more challenging head rotation problems and keeps a compact model size.作者: 職業(yè) 時(shí)間: 2025-3-27 09:15
A retrospective view of oral contraceptives, evaluate the model on the tasks of feature fusion and joint ordinal prediction of facial action units. Our experiments demonstrate the benefits of the proposed approach compared to the state of the art.作者: 增強(qiáng) 時(shí)間: 2025-3-27 09:50 作者: 首創(chuàng)精神 時(shí)間: 2025-3-27 17:36
Learning Action Concept Trees and Semantic Alignment Networks from Image-Description Dataopose a method to learn an Action Concept Tree (ACT) and an Action Semantic Alignment (ASA) model for classification from image-description data via a two-stage learning process. A new dataset for the task of . is built. Experimental results show that our method outperforms several baseline methods significantly.作者: 推崇 時(shí)間: 2025-3-27 20:48
Continuous Supervised Descent Method for Facial Landmark Localisationg RCPR, CGPRT, LBF, CFSS, and GSDM. Results upon both datasets show that the proposed method offers state-of-the-art performance on near frontal view data, improves state-of-the-art methods on more challenging head rotation problems and keeps a compact model size.作者: DEMN 時(shí)間: 2025-3-27 22:11 作者: PALL 時(shí)間: 2025-3-28 05:54 作者: 思鄉(xiāng)病 時(shí)間: 2025-3-28 08:43
Efficient Model Averaging for Deep Neural Networksopout, to encourage diversity of our sub-networks, we propose to maximize diversity of individual networks with a loss function: DivLoss. We demonstrate the effectiveness of DivLoss on the challenging CIFAR datasets.作者: dry-eye 時(shí)間: 2025-3-28 13:35
Computer Vision –ACCV 2016978-3-319-54184-6Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 惡心 時(shí)間: 2025-3-28 15:50 作者: 貪婪的人 時(shí)間: 2025-3-28 19:35
https://doi.org/10.1007/978-94-011-7701-6equires tremendous manual work, which is hard to scale up. Besides, the action categories in such datasets are pre-defined and vocabularies are fixed. However humans may describe the same action with different phrases, which leads to the difficulty of vocabulary expansion for traditional fully-super作者: entail 時(shí)間: 2025-3-29 00:57
Alison Gopnik,Andrew N. Meltzoffvarying illumination conditions, view angles, and surface reflectance. This is especially true for the challenging problem of pedestrian description in public spaces. We made two contributions in this study: (1) We contribute a large-scale pedestrian color naming dataset with 14,213 hand-labeled ima作者: 颶風(fēng) 時(shí)間: 2025-3-29 05:23
The Development of Word Meaninghowever, cause gait changes in appearance, which significantly drops performance of gait recognition. Considering a speed-invariant property at single-support phases where stride change due to speed changes are mitigated, and a stability against phase estimation error and segmentation noise by aggre作者: Fissure 時(shí)間: 2025-3-29 11:09
https://doi.org/10.1057/9781137486431grounds, articulation, varying body proportions, partial views and viewpoint changes. In this work we propose class-specific segmentation models that leverage parametric max-flow image segmentation and a large dataset of human shapes. Our contributions are as follows: (1) formulation of a sub-modula作者: cogitate 時(shí)間: 2025-3-29 14:49
https://doi.org/10.1007/978-3-319-02517-9 trying to recognise a small number of utterances in controlled environments (. digits and alphabets), partially due to the shortage of suitable datasets..We make two novel contributions: first, we develop a pipeline for fully automated large-scale data collection from TV broadcasts. With this we ha作者: 節(jié)約 時(shí)間: 2025-3-29 16:31 作者: Myocarditis 時(shí)間: 2025-3-29 21:29 作者: neutral-posture 時(shí)間: 2025-3-29 23:52
Safety Evaluation (Animal Studies),cognize the expression of humans and stylized characters independently. Then we utilize a transfer learning technique to learn the mapping from humans to characters to create a shared embedding feature space. This embedding also allows human expression-based image retrieval and character expression-作者: 小平面 時(shí)間: 2025-3-30 06:02 作者: Affluence 時(shí)間: 2025-3-30 11:41 作者: 收養(yǎng) 時(shí)間: 2025-3-30 14:16 作者: 離開真充足 時(shí)間: 2025-3-30 18:29
https://doi.org/10.1007/978-3-030-69105-9employing bagging or boosting to train several diverse models. For large neural networks, however, this is prohibitively expensive. To address this issue, we propose a method to leverage the benefits of ensembles without explicitely training several expensive neural network models. In contrast to Dr作者: inculpate 時(shí)間: 2025-3-30 21:06 作者: 巫婆 時(shí)間: 2025-3-31 02:37
https://doi.org/10.1007/978-3-319-91418-3rsity allows robustness to distractors and resistance against over-fitting, two valuable attributes of a competent classification solution. Its data-driven nature is comparable to deep convolutional neural networks, which elegantly blend global and local information through progressively more specif
