Zobrazeno 1 - 10
of 1 041
pro vyhledávání: '"HUANG Yufeng"'
Publikováno v:
Shiyan dongwu yu bijiao yixue, Vol 42, Iss 4, Pp 313-321 (2022)
Allergic contact dermatitis is a type Ⅳ hypersensitivity reaction caused by repeated skin exposure to a substance and is a common public health problem. Traditional skin sensitization tests are based on animal experiments such as guinea pig maximum
Externí odkaz:
https://doaj.org/article/a0660a95ad4741af86d9e16aec992c2e
Publikováno v:
Zeitschrift für Kristallographie - New Crystal Structures, Vol 234, Iss 4, Pp 665-667 (2019)
C25H6N8O2Cl4F12S2, monoclinic, C2/c (no. 15), a = 20.0584(9) Å, b = 16.1373(7) Å, c = 10.6232(5) Å, β = 93.238(4)°, V = 3433.1(3) Å3, Z = 8, Rgt(F) = 0.0543, wRref(F2) = 0.1427, T = 293(2) K.
Externí odkaz:
https://doaj.org/article/45f8352a0565487c8e0e6ff6ca92f84a
Autor:
Huang, Yufeng, Tang, Jiji, Chen, Zhuo, Zhang, Rongsheng, Zhang, Xinfeng, Chen, Weijie, Zhao, Zeng, Zhao, Zhou, Lv, Tangjie, Hu, Zhipeng, Zhang, Wen
Large-scale vision-language pre-training has achieved significant performance in multi-modal understanding and generation tasks. However, existing methods often perform poorly on image-text matching tasks that require structured representations, i.e.
Externí odkaz:
http://arxiv.org/abs/2305.06152
Autor:
Zhang, Wen, Zhu, Yushan, Chen, Mingyang, Geng, Yuxia, Huang, Yufeng, Xu, Yajing, Song, Wenting, Chen, Huajun
Knowledge graphs (KG) are essential background knowledge providers in many tasks. When designing models for KG-related tasks, one of the key tasks is to devise the Knowledge Representation and Fusion (KRF) module that learns the representation of ele
Externí odkaz:
http://arxiv.org/abs/2303.03922
Knowledge graph embedding (KGE), which maps entities and relations in a knowledge graph into continuous vector spaces, has achieved great success in predicting missing links in knowledge graphs. However, knowledge graphs often contain incomplete trip
Externí odkaz:
http://arxiv.org/abs/2301.00982
Autor:
Chen, Zhuo, Chen, Jiaoyan, Zhang, Wen, Guo, Lingbing, Fang, Yin, Huang, Yufeng, Zhang, Yichi, Geng, Yuxia, Pan, Jeff Z., Song, Wenting, Chen, Huajun
Publikováno v:
ACM MM 2023
Multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) whose entities are associated with relevant images. However, current MMEA algorithms rely on KG-level modality fusion strategies for multi
Externí odkaz:
http://arxiv.org/abs/2212.14454
Autor:
Chen, Zhuo, Zhang, Wen, Huang, Yufeng, Chen, Mingyang, Geng, Yuxia, Yu, Hongtao, Bi, Zhen, Zhang, Yichi, Yao, Zhen, Song, Wenting, Wu, Xinliang, Yang, Yi, Chen, Mingyi, Lian, Zhaoyang, Li, Yingying, Cheng, Lei, Chen, Huajun
In this work, we share our experience on tele-knowledge pre-training for fault analysis, a crucial task in telecommunication applications that requires a wide range of knowledge normally found in both machine log data and product documents. To organi
Externí odkaz:
http://arxiv.org/abs/2210.11298
Multi-modal aspect-based sentiment classification (MABSC) is task of classifying the sentiment of a target entity mentioned in a sentence and an image. However, previous methods failed to account for the fine-grained semantic association between the
Externí odkaz:
http://arxiv.org/abs/2208.09417
Autor:
Chen, Zhuo, Huang, Yufeng, Chen, Jiaoyan, Geng, Yuxia, Fang, Yin, Pan, Jeff, Zhang, Ningyu, Zhang, Wen
Visual question answering (VQA) often requires an understanding of visual concepts and language semantics, which relies on external knowledge. Most existing methods exploit pre-trained language models or/and unstructured text, but the knowledge in th
Externí odkaz:
http://arxiv.org/abs/2207.12888
Autor:
Chen, Zhuo, Huang, Yufeng, Chen, Jiaoyan, Geng, Yuxia, Zhang, Wen, Fang, Yin, Pan, Jeff Z., Chen, Huajun
Zero-shot learning (ZSL) aims to predict unseen classes whose samples have never appeared during training. One of the most effective and widely used semantic information for zero-shot image classification are attributes which are annotations for clas
Externí odkaz:
http://arxiv.org/abs/2207.01328