Zobrazeno 1 - 10
of 407
pro vyhledávání: '"Wang, Guangtao"'
In face detection, low-resolution faces, such as numerous small faces of a human group in a crowded scene, are common in dense face prediction tasks. They usually contain limited visual clues and make small faces less distinguishable from the other s
Externí odkaz:
http://arxiv.org/abs/2306.02277
Imbalanced node classification widely exists in real-world networks where graph neural networks (GNNs) are usually highly inclined to majority classes and suffer from severe performance degradation on classifying minority class nodes. Various imbalan
Externí odkaz:
http://arxiv.org/abs/2304.14635
In recent years, deep convolutional neural networks (CNN) have significantly advanced face detection. In particular, lightweight CNNbased architectures have achieved great success due to their lowcomplexity structure facilitating real-time detection
Externí odkaz:
http://arxiv.org/abs/2302.11816
Distilling supervision signal from a long sequence to make predictions is a challenging task in machine learning, especially when not all elements in the input sequence contribute equally to the desired output. In this paper, we propose SpanDrop, a s
Externí odkaz:
http://arxiv.org/abs/2208.02169
Publikováno v:
ACL 2022
Question answering over temporal knowledge graphs (KGs) efficiently uses facts contained in a temporal KG, which records entity relations and when they occur in time, to answer natural language questions (e.g., "Who was the president of the US before
Externí odkaz:
http://arxiv.org/abs/2203.00255
Autor:
Chen, Zheng, Wang, Guangtao
Publikováno v:
China Review, 2023 Feb 01. 23(1), 161-186.
Externí odkaz:
https://www.jstor.org/stable/48717992
Autor:
Sun, Peng, Lu, Chaoyang, Liang, Xiaoyu, Wang, Guangtao, Song, Changkun, Zhang, Quanguo, Zhang, Zhiping, Wang, Huanhuan, Alam, Mujeeb, Liu, Hong, Wang, Kaixin, Xia, Chenxi, Jiang, Danping
Publikováno v:
In Bioresource Technology September 2024 408
Publikováno v:
In Knowledge-Based Systems 8 April 2024 289
Recent work on aspect-level sentiment classification has demonstrated the efficacy of incorporating syntactic structures such as dependency trees with graph neural networks(GNN), but these approaches are usually vulnerable to parsing errors. To bette
Externí odkaz:
http://arxiv.org/abs/2103.11794
Autor:
Lv, Hua, Zhang, Fubiao, Wang, Lanlan, Shen, Qinhui, Li, Guanyong, Zhan, Mingyan, Wang, Gongke, Wang, Guangtao, Liu, Yumin
Publikováno v:
In Journal of Colloid And Interface Science January 2024 653 Part B:1304-1316