Zobrazeno 1 - 10
of 790
pro vyhledávání: '"Wang, Guoxin"'
Publikováno v:
Information Processing & Management, Volume 61, Issue 4, July 2024, 103716
In this paper, we propose a novel visual Semantic-Spatial Self-Highlighting Network (termed 3SHNet) for high-precision, high-efficiency and high-generalization image-sentence retrieval. 3SHNet highlights the salient identification of prominent object
Externí odkaz:
http://arxiv.org/abs/2404.17273
Autor:
Xiong, Yizhe, Chen, Hui, Hao, Tianxiang, Lin, Zijia, Han, Jungong, Zhang, Yuesong, Wang, Guoxin, Bao, Yongjun, Ding, Guiguang
Recently, the scale of transformers has grown rapidly, which introduces considerable challenges in terms of training overhead and inference efficiency in the scope of task adaptation. Existing works, namely Parameter-Efficient Fine-Tuning (PEFT) and
Externí odkaz:
http://arxiv.org/abs/2403.09192
Unsupervised learning methods have become increasingly important in deep learning due to their demonstrated large utilization of datasets and higher accuracy in computer vision and natural language processing tasks. There is a growing trend to extend
Externí odkaz:
http://arxiv.org/abs/2310.11153
Autor:
Wang, Guoxin, Cao, Xuyang, An, Shan, Fan, Fengmei, Zhang, Chao, Wang, Jinsong, Yu, Feng, Wang, Zhiren
Deep learning approaches, together with neuroimaging techniques, play an important role in psychiatric disorders classification. Previous studies on psychiatric disorders diagnosis mainly focus on using functional connectivity matrices of resting-sta
Externí odkaz:
http://arxiv.org/abs/2310.02690
Autor:
Lv, Tengchao, Huang, Yupan, Chen, Jingye, Cui, Lei, Ma, Shuming, Chang, Yaoyao, Huang, Shaohan, Wang, Wenhui, Dong, Li, Luo, Weiyao, Wu, Shaoxiang, Wang, Guoxin, Zhang, Cha, Wei, Furu
We present Kosmos-2.5, a multimodal literate model for machine reading of text-intensive images. Pre-trained on large-scale text-intensive images, Kosmos-2.5 excels in two distinct yet cooperative transcription tasks: (1) generating spatially-aware t
Externí odkaz:
http://arxiv.org/abs/2309.11419
Autor:
Tang, Zineng, Yang, Ziyi, Wang, Guoxin, Fang, Yuwei, Liu, Yang, Zhu, Chenguang, Zeng, Michael, Zhang, Cha, Bansal, Mohit
We propose Universal Document Processing (UDOP), a foundation Document AI model which unifies text, image, and layout modalities together with varied task formats, including document understanding and generation. UDOP leverages the spatial correlatio
Externí odkaz:
http://arxiv.org/abs/2212.02623
Despite several successes in document understanding, the practical task for long document understanding is largely under-explored due to several challenges in computation and how to efficiently absorb long multimodal input. Most current transformer-b
Externí odkaz:
http://arxiv.org/abs/2208.08201
Publikováno v:
In Journal of Industrial Information Integration July 2024 40
Publikováno v:
In Journal of Analytical and Applied Pyrolysis June 2024 180
Autor:
Wang, Guoxin, Fan, Fengmei, Shi, Sheng, An, Shan, Cao, Xuyang, Ge, Wenshu, Yu, Feng, Wang, Qi, Han, Xiaole, Tan, Shuping, Tan, Yunlong, Wang, Zhiren
Publikováno v:
In Computerized Medical Imaging and Graphics June 2024 114