標(biāo)題: Titlebook: Domain Adaptation in Computer Vision Applications; Gabriela Csurka Book 2017 Springer International Publishing AG 2017 Computer Vision.Vis [打印本頁] 作者: 歸納 時(shí)間: 2025-3-21 19:41
書目名稱Domain Adaptation in Computer Vision Applications影響因子(影響力)
書目名稱Domain Adaptation in Computer Vision Applications影響因子(影響力)學(xué)科排名
書目名稱Domain Adaptation in Computer Vision Applications網(wǎng)絡(luò)公開度
書目名稱Domain Adaptation in Computer Vision Applications網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Domain Adaptation in Computer Vision Applications被引頻次
書目名稱Domain Adaptation in Computer Vision Applications被引頻次學(xué)科排名
書目名稱Domain Adaptation in Computer Vision Applications年度引用
書目名稱Domain Adaptation in Computer Vision Applications年度引用學(xué)科排名
書目名稱Domain Adaptation in Computer Vision Applications讀者反饋
書目名稱Domain Adaptation in Computer Vision Applications讀者反饋學(xué)科排名
作者: Allure 時(shí)間: 2025-3-21 20:47
Marcelo C. Borba,Daniel C. Oreygeneralization of any learning method trained on a specific dataset. At the same time, with the rapid development of deep learning architectures, the activation values of Convolutional Neural Networks (CNN) are emerging as reliable and robust image descriptors. In this chapter we propose to verify t作者: Latency 時(shí)間: 2025-3-22 01:21 作者: 走路左晃右晃 時(shí)間: 2025-3-22 05:41
Reem Ashour,Sara Aldhaheri,Yasmeen Abu-Kheilpace Alignment (SA). They are based on a mapping function which aligns the source subspace with the target one, so as to obtain a domain invariant feature space. The solution of the corresponding optimization problem can be obtained in closed form, leading to a simple to implement and fast algorithm作者: 不能根除 時(shí)間: 2025-3-22 09:20 作者: manifestation 時(shí)間: 2025-3-22 16:36
https://doi.org/10.1007/978-3-031-32037-8e the joint distribution of samples and labels . in the source domain is assumed to be different, but related to that of a target domain ., but labels . are not available for the target set. This is a problem of Transductive Transfer Learning. In contrast to other methodologies in this book, our met作者: manifestation 時(shí)間: 2025-3-22 19:55
https://doi.org/10.1007/978-3-031-32338-6the discrepancy between their distributions and build representations common to both target and source domains. In reality, such a simplifying assumption rarely holds, since source data are routinely a subject of legal and contractual constraints between data owners and data customers. Despite a lim作者: diabetes 時(shí)間: 2025-3-22 21:30 作者: IRATE 時(shí)間: 2025-3-23 01:27 作者: 言外之意 時(shí)間: 2025-3-23 06:12
Elvia Giovanna Battaglia,Elisabetta Romautions. Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains. The approach implements this idea i作者: Graves’-disease 時(shí)間: 2025-3-23 11:32 作者: 樂意 時(shí)間: 2025-3-23 16:50 作者: 使痛苦 時(shí)間: 2025-3-23 22:04 作者: Vulnerable 時(shí)間: 2025-3-23 23:12
https://doi.org/10.1007/978-3-031-34398-8, indefinitely acquiring large amounts of annotations is not a sustainable process, and one can wonder if there exists a volume of annotations beyond which a task can be considered as solved or at least saturated. In this work, we study this crucial question for the task of . which are often seen as作者: depreciate 時(shí)間: 2025-3-24 04:31
Competing Ideals and an Emerging Consensusmage retrieval. Whereas the existing work mainly pursues utilizing attributes for various computer vision problems, we contend that the most basic problem—how to accurately and robustly detect attributes from images—has been left underexplored. Especially, the existing work rarely explicitly tackles作者: BLUSH 時(shí)間: 2025-3-24 09:03 作者: 粗野 時(shí)間: 2025-3-24 13:11 作者: CHAR 時(shí)間: 2025-3-24 15:36
Advances in Computer Vision and Pattern Recognitionhttp://image.papertrans.cn/e/image/282486.jpg作者: circuit 時(shí)間: 2025-3-24 19:13
https://doi.org/10.1007/978-3-319-58347-1Computer Vision; Visual Applications; Image Categorization; Pattern Recognition; Data Analytics; Unsuperv作者: Chemotherapy 時(shí)間: 2025-3-25 02:47 作者: 印第安人 時(shí)間: 2025-3-25 07:16 作者: 尾隨 時(shí)間: 2025-3-25 07:59 作者: 錢財(cái) 時(shí)間: 2025-3-25 11:54 作者: 脖子 時(shí)間: 2025-3-25 15:58
Unsupervised Domain Adaptation Based on Subspace Alignmentpace Alignment (SA). They are based on a mapping function which aligns the source subspace with the target one, so as to obtain a domain invariant feature space. The solution of the corresponding optimization problem can be obtained in closed form, leading to a simple to implement and fast algorithm作者: Phonophobia 時(shí)間: 2025-3-25 23:49
Learning Domain Invariant Embeddings by Matching Distributionsoach to addressing this problem therefore consists of learning an embedding of the source and target data such that they have similar distributions in the new space. In this chapter, we study several methods that follow this approach. At the core of these methods lies the notion of distance between 作者: Confidential 時(shí)間: 2025-3-26 00:55 作者: Prophylaxis 時(shí)間: 2025-3-26 07:38 作者: MUTED 時(shí)間: 2025-3-26 12:20
Correlation Alignment for Unsupervised Domain Adaptationift by aligning the second-order statistics of source and target distributions, without requiring any target labels. In contrast to subspace manifold methods, it aligns the original feature distributions of the source and target domains, rather than the bases of lower-dimensional subspaces. It is al作者: 歡呼 時(shí)間: 2025-3-26 13:59 作者: 真繁榮 時(shí)間: 2025-3-26 18:08
Domain-Adversarial Training of Neural Networksutions. Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains. The approach implements this idea i作者: 斗爭(zhēng) 時(shí)間: 2025-3-26 23:54
Unsupervised Fisher Vector Adaptation for Re-identificationthe unsupervised setting, i.e., when we do not have labeled data to adapt to the new conditions. Our focus in this work is on the Fisher Vector framework which has been shown to be a state-of-the-art patch encoding technique. Fisher Vectors primarily encode patch statistics by measuring first and se作者: 疼死我了 時(shí)間: 2025-3-27 02:20 作者: 排斥 時(shí)間: 2025-3-27 08:01 作者: diathermy 時(shí)間: 2025-3-27 09:52
Generalizing Semantic Part Detectors Across Domains, indefinitely acquiring large amounts of annotations is not a sustainable process, and one can wonder if there exists a volume of annotations beyond which a task can be considered as solved or at least saturated. In this work, we study this crucial question for the task of . which are often seen as作者: Gingivitis 時(shí)間: 2025-3-27 14:37
A Multisource Domain Generalization Approach to Visual Attribute Detectionmage retrieval. Whereas the existing work mainly pursues utilizing attributes for various computer vision problems, we contend that the most basic problem—how to accurately and robustly detect attributes from images—has been left underexplored. Especially, the existing work rarely explicitly tackles作者: 不法行為 時(shí)間: 2025-3-27 19:26 作者: TRAWL 時(shí)間: 2025-3-27 22:45
Applications of?UAVs in?Search and?Rescue hand, we propose . of a kernel that discriminatively combines multiple base GFKs to model the source and the target domains at fine-grained granularities. In particular, each base kernel pivots on a different set of landmarks—the most useful data instances that reveal the similarity between the sou作者: Ophthalmoscope 時(shí)間: 2025-3-28 03:16 作者: CHARM 時(shí)間: 2025-3-28 08:36 作者: 花束 時(shí)間: 2025-3-28 11:17 作者: atopic-rhinitis 時(shí)間: 2025-3-28 15:03 作者: xanthelasma 時(shí)間: 2025-3-28 20:53 作者: nonplus 時(shí)間: 2025-3-29 00:21
https://doi.org/10.1007/978-3-031-34398-8rning and DA techniques, and we study their generalization properties to parts from unseen classes when they are learned from a limited number of domains and example images. One of our conclusions is that, for a majority of the domains, part annotations transfer well, and that, performance of the se作者: Middle-Ear 時(shí)間: 2025-3-29 03:12
Geodesic Flow Kernel and Landmarks: Kernel Methods for Unsupervised Domain Adaptation hand, we propose . of a kernel that discriminatively combines multiple base GFKs to model the source and the target domains at fine-grained granularities. In particular, each base kernel pivots on a different set of landmarks—the most useful data instances that reveal the similarity between the sou作者: 財(cái)政 時(shí)間: 2025-3-29 08:41 作者: majestic 時(shí)間: 2025-3-29 12:31
Correlation Alignment for Unsupervised Domain Adaptationl but the number and dimensionality of target examples are very high. The resulting CORAL Linear Discriminant Analysis (CORAL-LDA)outperforms LDA by a large margin on standard domain adaptation benchmarks. Finally, we extend CORAL to learn a nonlinear transformation that aligns correlations of layer作者: 配偶 時(shí)間: 2025-3-29 16:37 作者: pantomime 時(shí)間: 2025-3-29 23:31 作者: GENRE 時(shí)間: 2025-3-30 00:12 作者: Middle-Ear 時(shí)間: 2025-3-30 07:55
Generalizing Semantic Part Detectors Across Domainsrning and DA techniques, and we study their generalization properties to parts from unseen classes when they are learned from a limited number of domains and example images. One of our conclusions is that, for a majority of the domains, part annotations transfer well, and that, performance of the se作者: palliative-care 時(shí)間: 2025-3-30 11:31
Book 2017ccess to the source data; discusses more recent deep DA methods, including discrepancy-based adaptation networks and adversarial discriminative DA models; addresses domain adaptation problems beyond image categorization, such as a Fisher encoding adaptation for vehicle re-identification, semantic se作者: 清洗 時(shí)間: 2025-3-30 16:04 作者: Glucose 時(shí)間: 2025-3-30 19:06
Reem Ashour,Sara Aldhaheri,Yasmeen Abu-Kheil and in particular on the KL divergence and the Hellinger distance. Throughout the chapter, we evaluate the different methods and distance measures on the task of visual object recognition and compare them against related baselines on a standard DA benchmark dataset.作者: 符合你規(guī)定 時(shí)間: 2025-3-30 21:02
Elvia Giovanna Battaglia,Elisabetta Romawed from the speech community. We explain under which conditions the domain influence is canceled out and show experimentally on two in-house license plate matching databases that the proposed approach improves accuracy.
