TY - JOUR
T1 - Editorial
T2 - Special Section on Intelligent Network Video Advances Based on Transformers
AU - Wu, Lin Yuanbo
AU - Li, Bo
AU - Wang, Huibing
AU - Shen, Chunhua
AU - Mora, Benjamin
AU - Chen, Chen
AU - Xie, Xianghua
N1 - Publisher Copyright:
© 2018 Tsinghua University Press.
PY - 2025
Y1 - 2025
N2 - Transformers, originally designed for natural language processing, have demonstrated remarkable capabilities in modeling long-range dependencies, capturing complex spatiotemporal patterns, and enhancing the interpretability of video-based AI systems. In recent years, the integration of transformer-based architectures has significantly advanced the field of intelligent network video analysis, reshaping traditional paradigms in video understanding, surveillance, and real-time processing.
AB - Transformers, originally designed for natural language processing, have demonstrated remarkable capabilities in modeling long-range dependencies, capturing complex spatiotemporal patterns, and enhancing the interpretability of video-based AI systems. In recent years, the integration of transformer-based architectures has significantly advanced the field of intelligent network video analysis, reshaping traditional paradigms in video understanding, surveillance, and real-time processing.
UR - http://www.scopus.com/inward/record.url?scp=105003127586&partnerID=8YFLogxK
U2 - 10.26599/BDMA.2025.9020026
DO - 10.26599/BDMA.2025.9020026
M3 - 社论
AN - SCOPUS:105003127586
SN - 2096-0654
VL - 8
SP - 519
JO - Big Data Mining and Analytics
JF - Big Data Mining and Analytics
IS - 3
ER -