Zobrazeno 1 - 10
of 98 416
pro vyhledávání: '"global/local"'
Autor:
Davvetas, Vasileios (AUTHOR) v.davvetas@leeds.ac.uk, Sichtmann, Christina (AUTHOR), Saridakis, Charalampos (AUTHOR), Diamantopoulos, Adamantios (AUTHOR)
Publikováno v:
Journal of International Marketing. Sep2023, Vol. 31 Issue 3, p19-40. 22p. 4 Charts, 5 Graphs.
HiFiSeg: High-Frequency Information Enhanced Polyp Segmentation with Global-Local Vision Transformer
Numerous studies have demonstrated the strong performance of Vision Transformer (ViT)-based methods across various computer vision tasks. However, ViT models often struggle to effectively capture high-frequency components in images, which are crucial
Externí odkaz:
http://arxiv.org/abs/2410.02528
In speaker tracking research, integrating and complementing multi-modal data is a crucial strategy for improving the accuracy and robustness of tracking systems. However, tracking with incomplete modalities remains a challenging issue due to noisy ob
Externí odkaz:
http://arxiv.org/abs/2408.14585
Gait recognition has attracted increasing attention from academia and industry as a human recognition technology from a distance in non-intrusive ways without requiring cooperation. Although advanced methods have achieved impressive success in lab sc
Externí odkaz:
http://arxiv.org/abs/2408.06834
Transformers have significantly advanced the field of 3D human pose estimation (HPE). However, existing transformer-based methods primarily use self-attention mechanisms for spatio-temporal modeling, leading to a quadratic complexity, unidirectional
Externí odkaz:
http://arxiv.org/abs/2408.03540
Autor:
Nie, Xiaodong1 (AUTHOR) xnie@uw.edu, Yang, Zhiyong2 (AUTHOR) z_yang4@uncg.edu, Zhang, Yinlong3 (AUTHOR) yinlong.zhang@utsa.edu, Janakiraman, Narayan4 (AUTHOR) janakira@uta.edu
Publikováno v:
Journal of Marketing Research (JMR). Jun2022, Vol. 59 Issue 3, p555-577. 23p. 1 Color Photograph, 2 Diagrams, 7 Charts, 3 Graphs.
Autor:
Xie, Fengxi1 (AUTHOR) fengxi.xie@campus.tu-berlin.de, Liang, Guozhen1 (AUTHOR) guozhen.liang@campus.tu-berlin.de, Chien, Ying-Ren2 (AUTHOR) yrchien@niu.edu.tw
Publikováno v:
Mathematics (2227-7390). Sep2024, Vol. 12 Issue 18, p2936. 14p.
Unlike images and natural language tokens, time series data is highly semantically sparse, resulting in labor-intensive label annotations. Unsupervised and Semi-supervised Domain Adaptation (UDA and SSDA) have demonstrated efficiency in addressing th
Externí odkaz:
http://arxiv.org/abs/2410.06671