標題: Titlebook: Computational Methods for Integrating Vision and Language; Kobus Barnard Book 2016 Springer Nature Switzerland AG 2016 [打印本頁] 作者: 女孩 時間: 2025-3-21 19:06
書目名稱Computational Methods for Integrating Vision and Language影響因子(影響力)
書目名稱Computational Methods for Integrating Vision and Language影響因子(影響力)學科排名
書目名稱Computational Methods for Integrating Vision and Language網(wǎng)絡公開度
書目名稱Computational Methods for Integrating Vision and Language網(wǎng)絡公開度學科排名
書目名稱Computational Methods for Integrating Vision and Language被引頻次
書目名稱Computational Methods for Integrating Vision and Language被引頻次學科排名
書目名稱Computational Methods for Integrating Vision and Language年度引用
書目名稱Computational Methods for Integrating Vision and Language年度引用學科排名
書目名稱Computational Methods for Integrating Vision and Language讀者反饋
書目名稱Computational Methods for Integrating Vision and Language讀者反饋學科排名
作者: cavity 時間: 2025-3-21 22:10 作者: 意外的成功 時間: 2025-3-22 01:36
Sources of Data for Linking Visual and Linguistic Information,e I catalog many of the data sets that have been used. I begin with the WordNet text resource, which is commonly used to anchor text in datasets with respect to semantics, as well being used for preprocessing (Chapter 5) and joint learning. I then describe datasets that provide images or videos toge作者: 關心 時間: 2025-3-22 06:54
Extracting and Representing Visual Information, scope. For example, semantics can pertain to the entire scene (e.g., birthday, sunset, frightening), objects within (cars, people, dogs), parts of objects, backgrounds (e.g., sky, water), and even spatial relations between objects or backgrounds. Given appropriate localization, the appearance of ob作者: CLAN 時間: 2025-3-22 10:27 作者: 疏遠天際 時間: 2025-3-22 13:00
Modeling Images and Keywords,lenging. The underlying goal.no less than jointly understanding vision and language.is vast, and progress reflects the need for researchers to focus on manageable sub-problems. Historically, one clear trend is increasingly sophisticated language modeling, which is our first organizing principle. Thi作者: 疏遠天際 時間: 2025-3-22 19:18 作者: Hectic 時間: 2025-3-22 21:23 作者: chisel 時間: 2025-3-23 02:43
Computational Methods for Integrating Vision and Language978-3-031-01814-5Series ISSN 2153-1056 Series E-ISSN 2153-1064 作者: Delude 時間: 2025-3-23 08:33 作者: 評論性 時間: 2025-3-23 10:43 作者: reperfusion 時間: 2025-3-23 16:43
Subjectivity in the American Protest Novelage. The different modalities reinforce and complement each other, and provide for more effective understanding of the world around us, provided that we can integrate the information into a common representation or abstract understanding. Similarly, information from multiple modalities can be exploi作者: 有惡意 時間: 2025-3-23 20:11 作者: Heretical 時間: 2025-3-23 22:44 作者: 樹木中 時間: 2025-3-24 02:24
Laura Rojas Vidaurreta,Jonatas Maia da Costa scope. For example, semantics can pertain to the entire scene (e.g., birthday, sunset, frightening), objects within (cars, people, dogs), parts of objects, backgrounds (e.g., sky, water), and even spatial relations between objects or backgrounds. Given appropriate localization, the appearance of ob作者: agnostic 時間: 2025-3-24 09:58
Perspectives in Cultural-Historical Research), tags and keywords (largely concrete nouns) for images (§3.4) and video frames, natural language captions for images (§3.5), text found near images in multimodal documents (e.g., Wikipedia pages), closed captioning for audio-video data available as a text stream (§5.1), text extracted from the spe作者: 耐寒 時間: 2025-3-24 14:41
Pilar de Almeida,Luciana Soares Munizlenging. The underlying goal.no less than jointly understanding vision and language.is vast, and progress reflects the need for researchers to focus on manageable sub-problems. Historically, one clear trend is increasingly sophisticated language modeling, which is our first organizing principle. Thi作者: 輕推 時間: 2025-3-24 18:18
https://doi.org/10.1057/9781137425997for jointly modeling visual and linguistic data has focused on keywords for images, there is much to be gained by going beyond keywords. Images with full text captions are common, and such captions typically contain deeper semantic information than curated keywords or user supplied tags (e.g., Flick作者: Canyon 時間: 2025-3-24 20:26
The Importance of Hegelian Recognition,anguage pre-processing might have been used to extract language components, the subsequent integration of vision and language described largely ignored the ordering of language data. However, order matters in written text, much as spatial arrangement matters in visual data. Further, in video, narrat作者: Consequence 時間: 2025-3-25 01:08 作者: 獎牌 時間: 2025-3-25 06:04
Laura Rojas Vidaurreta,Jonatas Maia da Costa, perhaps the scene has people and cars) carries meaning. By contrast, a single pixel or region does not tell us much about what was in front of the camera when the picture was taken. In short, for many tasks, our representations need to support localization and context.作者: 開始從未 時間: 2025-3-25 09:39 作者: 左右連貫 時間: 2025-3-25 14:12
The Importance of Hegelian Recognition,dering; (2) producing sequential output (e.g., image and video captioning); and (3) interpreting more complex queries for image search and visual question and answering. Some of these efforts are covered in this chapter.作者: 不容置疑 時間: 2025-3-25 16:34
Extracting and Representing Visual Information,, perhaps the scene has people and cars) carries meaning. By contrast, a single pixel or region does not tell us much about what was in front of the camera when the picture was taken. In short, for many tasks, our representations need to support localization and context.作者: Accommodation 時間: 2025-3-25 23:35 作者: alcohol-abuse 時間: 2025-3-26 01:26
Sequential Structure,dering; (2) producing sequential output (e.g., image and video captioning); and (3) interpreting more complex queries for image search and visual question and answering. Some of these efforts are covered in this chapter.作者: GREEN 時間: 2025-3-26 07:34 作者: fabricate 時間: 2025-3-26 10:06
Subjectivity in the American Protest Novelta, training systems to extract semantic content from either visual and linguistic data, and develop machine representations that are indicative of higher level semantics and thus can support intelligent machine behavior.作者: 刪減 時間: 2025-3-26 14:40
Introduction,ta, training systems to extract semantic content from either visual and linguistic data, and develop machine representations that are indicative of higher level semantics and thus can support intelligent machine behavior.作者: 多節(jié) 時間: 2025-3-26 17:55
2153-1056 l applications. Examples of dual visual-linguistic data includes images with keywords, video with narrative, and figures in documents. We consider two key task-driven themes: translating from one modality to another (e.g., inferring annotations for images) and understanding the data using all modali作者: 飛行員 時間: 2025-3-27 00:04 作者: 抗原 時間: 2025-3-27 04:15
Perspectives in Cultural-Historical Researche standard loosely labeled data as illustrated in Figure 1.8), bounding boxes or polygons for pertinent objects within the images, and relatively complete region level segmentations covering most the major elements of each image with a single semantic label for each region. The breakdown also applie作者: 顯赫的人 時間: 2025-3-27 08:51 作者: infarct 時間: 2025-3-27 11:43 作者: TATE 時間: 2025-3-27 15:00
Sources of Data for Linking Visual and Linguistic Information,e standard loosely labeled data as illustrated in Figure 1.8), bounding boxes or polygons for pertinent objects within the images, and relatively complete region level segmentations covering most the major elements of each image with a single semantic label for each region. The breakdown also applie作者: Kindle 時間: 2025-3-27 21:33
Text and Speech Processing,tations of the semantics in linguistic data using conventions about language structure, information from lexical resources (§5.5), and context from world knowledge in general. Even in the case of images with keywords, pre-processing can be used to remove less useful words, flag proper nouns, identif作者: Living-Will 時間: 2025-3-27 22:51 作者: 迎合 時間: 2025-3-28 04:17
2153-1056 ds toward natural language, and ones considering sequential aspects of natural language. Methods for keywords are further organized based on localization of sem978-3-031-00686-9978-3-031-01814-5Series ISSN 2153-1056 Series E-ISSN 2153-1064 作者: MITE 時間: 2025-3-28 10:18
Book 2016e information is complementary. Computational methods discussed are broadly organized into ones forsimple keywords, ones going beyond keywords toward natural language, and ones considering sequential aspects of natural language. Methods for keywords are further organized based on localization of sem作者: 分期付款 時間: 2025-3-28 13:04 作者: Conscientious 時間: 2025-3-28 15:00
