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pro vyhledávání: '"Xiong, Shengwu"'
Although the Prototypical Network (ProtoNet) has demonstrated effectiveness in few-shot biological event detection, two persistent issues remain. Firstly, there is difficulty in constructing a representative negative prototype due to the absence of e
Externí odkaz:
http://arxiv.org/abs/2409.15168
Visual grounding is the task of locating objects specified by natural language expressions. Existing methods extend generic object detection frameworks to tackle this task. They typically extract visual and textual features separately using independe
Externí odkaz:
http://arxiv.org/abs/2409.04999
The absence of real targets to guide the model training is one of the main problems with the makeup transfer task. Most existing methods tackle this problem by synthesizing pseudo ground truths (PGTs). However, the generated PGTs are often sub-optima
Externí odkaz:
http://arxiv.org/abs/2405.17240
Autor:
Tonja, Atnafu Lambebo, Azime, Israel Abebe, Belay, Tadesse Destaw, Yigezu, Mesay Gemeda, Mehamed, Moges Ahmed, Ayele, Abinew Ali, Jibril, Ebrahim Chekol, Woldeyohannis, Michael Melese, Kolesnikova, Olga, Slusallek, Philipp, Klakow, Dietrich, Xiong, Shengwu, Yimam, Seid Muhie
Large language models (LLMs) have gained popularity recently due to their outstanding performance in various downstream Natural Language Processing (NLP) tasks. However, low-resource languages are still lagging behind current state-of-the-art (SOTA)
Externí odkaz:
http://arxiv.org/abs/2403.13737
Self-supervised learning (SSL) techniques have recently been integrated into the few-shot learning (FSL) framework and have shown promising results in improving the few-shot image classification performance. However, existing SSL approaches used in F
Externí odkaz:
http://arxiv.org/abs/2304.13287
Makeup transfer is not only to extract the makeup style of the reference image, but also to render the makeup style to the semantic corresponding position of the target image. However, most existing methods focus on the former and ignore the latter,
Externí odkaz:
http://arxiv.org/abs/2112.03631
Publikováno v:
In Engineering Applications of Artificial Intelligence December 2024 138 Part A
Autor:
Liu, Bingyi, Fang, Zhipeng, Wang, Wei, Shao, Xun, Wei, Wei, Jia, Dongyao, Wang, Enshu, Xiong, Shengwu
Green Vehicular Ad-hoc Network (VANET) is a newly-emerged research area which focuses on reducing harmful impacts of vehicular communication equipments on the natural environment. Recent studies have shown that grouping vehicles into clusters for gre
Externí odkaz:
http://arxiv.org/abs/2110.02565
Autor:
Zi, Yunfei, Xiong, Shengwu
Publikováno v:
In Engineering Applications of Artificial Intelligence July 2024 133 Part A
Publikováno v:
In Knowledge-Based Systems 21 June 2024 294