Titlebook: Advances in Multimodal Information Retrieval and Generation; Man Luo,Tejas Gokhale,Chitta Baral Book 2025 The Editor(s) (if applicable) an

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書目名稱Advances in Multimodal Information Retrieval and Generation影響因子(影響力)




書目名稱Advances in Multimodal Information Retrieval and Generation影響因子(影響力)學(xué)科排名




書目名稱Advances in Multimodal Information Retrieval and Generation網(wǎng)絡(luò)公開度




書目名稱Advances in Multimodal Information Retrieval and Generation網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Advances in Multimodal Information Retrieval and Generation被引頻次




書目名稱Advances in Multimodal Information Retrieval and Generation被引頻次學(xué)科排名




書目名稱Advances in Multimodal Information Retrieval and Generation年度引用




書目名稱Advances in Multimodal Information Retrieval and Generation年度引用學(xué)科排名




書目名稱Advances in Multimodal Information Retrieval and Generation讀者反饋




書目名稱Advances in Multimodal Information Retrieval and Generation讀者反饋學(xué)科排名

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978-3-031-57818-2The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Man Luo,Tejas Gokhale,Chitta BaralProvides a comprehensive overview of the state-of-the-art in multi-modal architectures and representation learning.Presents state-of-the-art techniques including neural models based on transformers an

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Synthesis Lectures on Computer Visionhttp://image.papertrans.cn/b/image/167290.jpg

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https://doi.org/10.1007/978-3-8351-9208-9In this chapter, we will learn about the modeling and learning techniques that drive multimodal applications. We will focus specifically on the recent advances in transformer-based modeling for natural language understanding, and image understanding, and how these approaches connect for jointly understanding combinations of language and image.

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https://doi.org/10.1007/978-3-8351-9208-9, limited to a single type of data, often fall short of capturing the complexity and richness of human communication and experience. In contrast, multimodal retrieval systems leverage the complementary nature of different data types to provide more accurate, context-aware, and user-centric search re

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https://doi.org/10.1007/978-3-8351-9208-9t. With the proliferation of multimedia platforms and data sources, we are constantly bombarded with a rich variety of images, videos, audio, and text. This vast array of heterogeneous data poses new challenges and opportunities for the field of Information Retrieval (IR). To address these challenge

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