Zobrazeno 1 - 10
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pro vyhledávání: '"context model"'
Efficient storage of large-scale point cloud data has become increasingly challenging due to advancements in scanning technology. Recent deep learning techniques have revolutionized this field; However, most existing approaches rely on single-modalit
Externí odkaz:
http://arxiv.org/abs/2409.12724
Recently, 3D Gaussian Splatting (3DGS) has become a promising framework for novel view synthesis, offering fast rendering speeds and high fidelity. However, the large number of Gaussians and their associated attributes require effective compression t
Externí odkaz:
http://arxiv.org/abs/2405.20721
Accurate utterance classification in motivational interviews is crucial to automatically understand the quality and dynamics of client-therapist interaction, and it can serve as a key input for systems mediating such interactions. Motivational interv
Externí odkaz:
http://arxiv.org/abs/2404.03312
Deep learning-based image compression has made great progresses recently. However, many leading schemes use serial context-adaptive entropy model to improve the rate-distortion (R-D) performance, which is very slow. In addition, the complexities of t
Externí odkaz:
http://arxiv.org/abs/2309.02529
Akademický článek
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Autor:
Busso, Matteo, Li, Xiaoyue
Diversity-aware data are essential for a robust modeling of human behavior in context. In addition, being the human behavior of interest for numerous applications, data must also be reusable across domain, to ensure diversity of interpretations. Curr
Externí odkaz:
http://arxiv.org/abs/2306.09753
Autor:
Spyratos, Nicolas
We propose a novel database model whose basic structure is a labeled, directed, acyclic graph with a single root, in which the nodes represent the data sets of an application and the edges represent functional relationships among the data sets. We ca
Externí odkaz:
http://arxiv.org/abs/2305.13895
Autor:
Nguyen, Thong, Wu, Xiaobao, Dong, Xinshuai, Luu, Anh Tuan, Nguyen, Cong-Duy, Hai, Zhen, Bing, Lidong
Multimodal Review Helpfulness Prediction (MRHP) aims to rank product reviews based on predicted helpfulness scores and has been widely applied in e-commerce via presenting customers with useful reviews. Previous studies commonly employ fully-connecte
Externí odkaz:
http://arxiv.org/abs/2305.12678
Akademický článek
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Autor:
Meyer, Anna, Kaup, André
In contrast to traditional compression techniques performing linear transforms, the latent space of popular compressive autoencoders is obtained from a learned nonlinear mapping and hard to interpret. In this paper, we explore a promising alternative
Externí odkaz:
http://arxiv.org/abs/2303.05121