Zobrazeno 1 - 10
of 57
pro vyhledávání: '"Wang Yongpan"'
Autor:
Li, Siyuan, Li, Yuekang, Chen, Zuxin, Dong, Chaopeng, Wang, Yongpan, Li, Hong, Chen, Yongle, Zhu, Hongsong
Code reuse in software development frequently facilitates the spread of vulnerabilities, making the scope of affected software in CVE reports imprecise. Traditional methods primarily focus on identifying reused vulnerability code within target softwa
Externí odkaz:
http://arxiv.org/abs/2411.18347
Autor:
Wang, Yongpan, Li, Hong, Zhu, Xiaojie, Li, Siyuan, Dong, Chaopeng, Yang, Shouguo, Qin, Kangyuan
Binary code search plays a crucial role in applications like software reuse detection. Currently, existing models are typically based on either internal code semantics or a combination of function call graphs (CG) and internal code semantics. However
Externí odkaz:
http://arxiv.org/abs/2411.01102
Autor:
Li, Siyuan, Wang, Yongpan, Dong, Chaopeng, Yang, Shouguo, Li, Hong, Sun, Hao, Lang, Zhe, Chen, Zuxin, Wang, Weijie, Zhu, Hongsong, Sun, Limin
Third-party libraries (TPLs) are extensively utilized by developers to expedite the software development process and incorporate external functionalities. Nevertheless, insecure TPL reuse can lead to significant security risks. Existing methods are e
Externí odkaz:
http://arxiv.org/abs/2305.04026
Despite the success of deep neural network (DNN) on sequential data (i.e., scene text and speech) recognition, it suffers from the over-confidence problem mainly due to overfitting in training with the cross-entropy loss, which may make the decision-
Externí odkaz:
http://arxiv.org/abs/2303.06946
Semantic information has been proved effective in scene text recognition. Most existing methods tend to couple both visual and semantic information in an attention-based decoder. As a result, the learning of semantic features is prone to have a bias
Externí odkaz:
http://arxiv.org/abs/2111.12351
Autor:
Li, Chengxi, Gao, Feiyu, Bu, Jiajun, Xu, Lu, Chen, Xiang, Gu, Yu, Shao, Zirui, Zheng, Qi, Zhang, Ningyu, Wang, Yongpan, Yu, Zhi
Aspect-based sentiment analysis (ABSA) is an emerging fine-grained sentiment analysis task that aims to extract aspects, classify corresponding sentiment polarities and find opinions as the causes of sentiment. The latest research tends to solve the
Externí odkaz:
http://arxiv.org/abs/2109.08306
This paper tackles the problem of table structure parsing (TSP) from images in the wild. In contrast to existing studies that mainly focus on parsing well-aligned tabular images with simple layouts from scanned PDF documents, we aim to establish a pr
Externí odkaz:
http://arxiv.org/abs/2109.02199
Autor:
Wang, Tianwei, Zhu, Yuanzhi, Jin, Lianwen, Peng, Dezhi, Li, Zhe, He, Mengchao, Wang, Yongpan, Luo, Canjie
Text recognition is a popular research subject with many associated challenges. Despite the considerable progress made in recent years, the text recognition task itself is still constrained to solve the problem of reading cropped line text images and
Externí odkaz:
http://arxiv.org/abs/2106.05920
Autor:
He, Minghang, Liao, Minghui, Yang, Zhibo, Zhong, Humen, Tang, Jun, Cheng, Wenqing, Yao, Cong, Wang, Yongpan, Bai, Xiang
Over the past few years, the field of scene text detection has progressed rapidly that modern text detectors are able to hunt text in various challenging scenarios. However, they might still fall short when handling text instances of extreme aspect r
Externí odkaz:
http://arxiv.org/abs/2104.01070
In this paper, we propose an end-to-end trainable framework for restoring historical documents content that follows the correct reading order. In this framework, two branches named character branch and layout branch are added behind the feature extra
Externí odkaz:
http://arxiv.org/abs/2007.06890