標題: Titlebook: Domain Adaptation for Visual Understanding; Richa Singh,Mayank Vatsa,Nalini Ratha Book 2020 Springer Nature Switzerland AG 2020 Domain Ada [打印本頁] 作者: 要求 時間: 2025-3-21 17:46
書目名稱Domain Adaptation for Visual Understanding影響因子(影響力)
書目名稱Domain Adaptation for Visual Understanding影響因子(影響力)學(xué)科排名
書目名稱Domain Adaptation for Visual Understanding網(wǎng)絡(luò)公開度
書目名稱Domain Adaptation for Visual Understanding網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Domain Adaptation for Visual Understanding被引頻次
書目名稱Domain Adaptation for Visual Understanding被引頻次學(xué)科排名
書目名稱Domain Adaptation for Visual Understanding年度引用
書目名稱Domain Adaptation for Visual Understanding年度引用學(xué)科排名
書目名稱Domain Adaptation for Visual Understanding讀者反饋
書目名稱Domain Adaptation for Visual Understanding讀者反饋學(xué)科排名
作者: 方舟 時間: 2025-3-21 21:09 作者: Congeal 時間: 2025-3-22 03:04
Book 2020cy between the source and target data to enhance image classification performance; presentsa technique for multi-modal fusion that enhances facial action recognition, and a framework for intuition learning in domain adaptation; examines an original interpolation-based approach to address the issue o作者: 環(huán)形 時間: 2025-3-22 06:21 作者: FID 時間: 2025-3-22 10:28
Multi-modal Conditional Feature Enhancement for Facial Action Unit Recognition,erformance. We apply our fusion method to the task of facial action unit?(AU) recognition by learning to enhance the thermal and visible feature representations. We compare our approach to other recent fusion schemes and demonstrate its effectiveness on the MMSE dataset by outperforming previous tec作者: homocysteine 時間: 2025-3-22 14:47
sa technique for multi-modal fusion that enhances facial action recognition, and a framework for intuition learning in domain adaptation; examines an original interpolation-based approach to address the issue o978-3-030-30673-1978-3-030-30671-7作者: homocysteine 時間: 2025-3-22 19:49 作者: Panther 時間: 2025-3-22 23:01
M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning,fy an unlabeled “target” dataset by leveraging a labeled “source” dataset that comes from a slightly similar distribution. We propose metric-based adversarial discriminative domain adaptation?(M-ADDA) which performs two main steps. First, it uses a metric learning approach to train the source model 作者: 充氣球 時間: 2025-3-23 02:40 作者: 緯線 時間: 2025-3-23 08:26 作者: Blasphemy 時間: 2025-3-23 11:37
Cross-Modality Video Segment Retrieval with Ensemble Learning,ared with video language retrieval, video segment retrieval?is a novel task that uses natural language to retrieve a specific video segment from the whole video. One common method is to learn a similarity metric between video and language features. In this chapter, we utilize ensemble learning?metho作者: Herbivorous 時間: 2025-3-23 16:28 作者: Flu表流動 時間: 2025-3-23 19:06
Multi-modal Conditional Feature Enhancement for Facial Action Unit Recognition,are mapped for the goal of obtaining performance improvements by combining the individual modalities. Often, these heavily fine-tuned feature?representations would have strong feature discriminability in their own spaces which may not be present in the fused subspace owing to the compression of info作者: 悄悄移動 時間: 2025-3-23 23:44
Intuition Learning,. but I have an . that?this research might get accepted”. Intuition?is often employed by humans to solve challenging problems without explicit efforts. Intuition?is not trained but is learned from one’s own experience and observation. The aim of this research is to provide . to an algorithm, apart f作者: 牙齒 時間: 2025-3-24 04:16
Alleviating Tracking Model Degradation Using Interpolation-Based Progressive Updating,e one model degradation problem: With low learning rate, the tracking model cannot be updated as fast as the large-scale variation or deformation of fast motion targets; As for high learning rate, the tracking model is not robust enough against disturbance, such as occlusion. To enable the tracking 作者: 種屬關(guān)系 時間: 2025-3-24 08:36 作者: Crepitus 時間: 2025-3-24 11:38 作者: 致詞 時間: 2025-3-24 18:21 作者: 群島 時間: 2025-3-24 19:24 作者: anachronistic 時間: 2025-3-25 01:33
Alan Elbaum,Lucia Kinsey,Jeffrey Marianote our method on the task of the video clip retrieval with the new proposed Distinct Describable Moments dataset. Extensive experiments have shown that our approach achieves improvement compared with the result of the state-of-art.作者: Lipoma 時間: 2025-3-25 05:06 作者: 一再遛 時間: 2025-3-25 08:56
XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings,ned embedding to preserve semantics shared across domains. We report promising qualitative results for the task of face-to-cartoon translation. The cartoon dataset we collected for this purpose, “CartoonSet”, is also publicly available as a new benchmark for semantic style transfer?at ..作者: Charitable 時間: 2025-3-25 13:28 作者: foliage 時間: 2025-3-25 16:06
Cross-Modality Video Segment Retrieval with Ensemble Learning,te our method on the task of the video clip retrieval with the new proposed Distinct Describable Moments dataset. Extensive experiments have shown that our approach achieves improvement compared with the result of the state-of-art.作者: 運氣 時間: 2025-3-25 21:56 作者: 彎彎曲曲 時間: 2025-3-26 01:18
Adam Palmquist,Izabella Jedel,Ole Goetheth a two-stream Convolutional Neural Network (CNN). We demonstrate the ability of the proposed approach to achieve state-of-the-art performance for image classification?on three benchmark domain adaptation?datasets: Office-31 [.], Office-Home [.] and Office-Caltech [.].作者: 流行 時間: 2025-3-26 08:01
The Attainable Game Experience Frameworking function using unlabeled data. The mapping functions and feature representation are succinct and can be used to supplement any supervised or semi-supervised algorithm. The experiments on the CIFAR-10 database show challenging cases where intuition learning improves the performance of a given classifier.作者: FUSC 時間: 2025-3-26 12:22 作者: Expiration 時間: 2025-3-26 16:08
On Minimum Discrepancy Estimation for Deep Domain Adaptation,th a two-stream Convolutional Neural Network (CNN). We demonstrate the ability of the proposed approach to achieve state-of-the-art performance for image classification?on three benchmark domain adaptation?datasets: Office-31 [.], Office-Home [.] and Office-Caltech [.].作者: 未成熟 時間: 2025-3-26 19:17
Intuition Learning,ing function using unlabeled data. The mapping functions and feature representation are succinct and can be used to supplement any supervised or semi-supervised algorithm. The experiments on the CIFAR-10 database show challenging cases where intuition learning improves the performance of a given classifier.作者: 態(tài)學(xué) 時間: 2025-3-27 00:10
Alleviating Tracking Model Degradation Using Interpolation-Based Progressive Updating,es, the obtained intermediate response map can fit the learning rate well, which will effectively alleviate the learning-related model degradation. The evaluations on the benchmark datasets KITTI and VOT2017 demonstrate that the proposed tracker outperforms the existing CF-based models, with advantages regarding the tracking accuracy.作者: 過多 時間: 2025-3-27 02:34 作者: canonical 時間: 2025-3-27 06:24 作者: 流動才波動 時間: 2025-3-27 10:22
Richa Singh,Mayank Vatsa,Nalini RathaPresents the latest research on domain adaptation for visual understanding.Provides perspectives from an international selection of authorities in the field.Reviews a variety of applications and techn作者: pellagra 時間: 2025-3-27 15:19 作者: palpitate 時間: 2025-3-27 18:12 作者: 轉(zhuǎn)向 時間: 2025-3-27 23:26
Decision-Making Across Culturesfy an unlabeled “target” dataset by leveraging a labeled “source” dataset that comes from a slightly similar distribution. We propose metric-based adversarial discriminative domain adaptation?(M-ADDA) which performs two main steps. First, it uses a metric learning approach to train the source model 作者: Excise 時間: 2025-3-28 05:11
Satheesh Gunaga,Jonathan Zygowiecpping between the corpus-level style of each collection, while preserving semantic content shared across the two domains. We introduce ., a dual adversarial auto-encoder, which captures a shared representation of the common domain semantic content in an unsupervised way, while jointly learning the d作者: Ingredient 時間: 2025-3-28 09:24
Shauna Gibbons,Christian T. Sinclairearns the small dataset as a transfer task from a larger source dataset. Transfer Learning?can deliver higher accuracy if the hyperparameters and source dataset are chosen well. One of the important parameters is the learning rate for the layers of the neural network. We show through experiments on 作者: ACE-inhibitor 時間: 2025-3-28 11:21
Alan Elbaum,Lucia Kinsey,Jeffrey Marianoared with video language retrieval, video segment retrieval?is a novel task that uses natural language to retrieve a specific video segment from the whole video. One common method is to learn a similarity metric between video and language features. In this chapter, we utilize ensemble learning?metho作者: 輕率的你 時間: 2025-3-28 16:25
Adam Palmquist,Izabella Jedel,Ole Goethely in object classification?and recognition tasks. However, DL cannot always perform well when the training and testing images come from different distributions or in the presence of domain shift between training and testing images. They also suffer in the absence of labeled input data. Domain adapt作者: 泥沼 時間: 2025-3-28 21:52 作者: cancer 時間: 2025-3-28 23:57 作者: TATE 時間: 2025-3-29 05:35 作者: 盟軍 時間: 2025-3-29 09:01
https://doi.org/10.1007/978-3-658-30265-8Kulturbetriebslehre; Wirtschaftswissenschaft; Sozialwissenschaft; Kulturwissenschaft; Kulturgüter; Bildun
