Zobrazeno 1 - 10
of 180
pro vyhledávání: '"Chen, Zhineng"'
Deep neural networks (DNNs) are known to be vulnerable to adversarial attacks even under a black-box setting where the adversary can only query the model. Particularly, query-based black-box adversarial attacks estimate adversarial gradients based on
Externí odkaz:
http://arxiv.org/abs/2408.01978
Scene Text Recognition (STR) methods have demonstrated robust performance in word-level text recognition. However, in applications the text image is sometimes long due to detected with multiple horizontal words. It triggers the requirement to build l
Externí odkaz:
http://arxiv.org/abs/2407.12317
Autor:
Wang, Jiaqi, Zang, Yuhang, Zhang, Pan, Chu, Tao, Cao, Yuhang, Sun, Zeyi, Liu, Ziyu, Dong, Xiaoyi, Wu, Tong, Lin, Dahua, Chen, Zeming, Wang, Zhi, Meng, Lingchen, Yao, Wenhao, Yang, Jianwei, Wu, Sihong, Chen, Zhineng, Wu, Zuxuan, Jiang, Yu-Gang, Wu, Peixi, Chai, Bosong, Nie, Xuan, Yan, Longquan, Wang, Zeyu, Zhou, Qifan, Wang, Boning, Huang, Jiaqi, Xu, Zunnan, Li, Xiu, Yuan, Kehong, Zu, Yanyan, Ha, Jiayao, Gao, Qiong, Jiao, Licheng
Detecting objects in real-world scenes is a complex task due to various challenges, including the vast range of object categories, and potential encounters with previously unknown or unseen objects. The challenges necessitate the development of publi
Externí odkaz:
http://arxiv.org/abs/2406.11739
Images suffer from heavy spatial redundancy because pixels in neighboring regions are spatially correlated. Existing approaches strive to overcome this limitation by reducing less meaningful image regions. However, current leading methods rely on sup
Externí odkaz:
http://arxiv.org/abs/2404.00680
Multi-modal models show appealing performance in visual recognition tasks recently, as free-form text-guided training evokes the ability to understand fine-grained visual content. However, current models are either inefficient or cannot be trivially
Externí odkaz:
http://arxiv.org/abs/2401.17851
3D content creation via text-driven stylization has played a fundamental challenge to multimedia and graphics community. Recent advances of cross-modal foundation models (e.g., CLIP) have made this problem feasible. Those approaches commonly leverage
Externí odkaz:
http://arxiv.org/abs/2311.05464
Autor:
Du, Yongkun, Chen, Zhineng, Jia, Caiyan, Yin, Xiaoting, Li, Chenxia, Du, Yuning, Jiang, Yu-Gang
Scene text recognition (STR) methods have struggled to attain high accuracy and fast inference speed. Autoregressive (AR)-based models implement the recognition in a character-by-character manner, showing superiority in accuracy but with slow inferen
Externí odkaz:
http://arxiv.org/abs/2307.12270
Autor:
Su, Yuchen, Chen, Zhineng, Shao, Zhiwen, Du, Yuning, Ji, Zhilong, Bai, Jinfeng, Zhou, Yong, Jiang, Yu-Gang
Recently, regression-based methods, which predict parameterized text shapes for text localization, have gained popularity in scene text detection. However, the existing parameterized text shape methods still have limitations in modeling arbitrary-sha
Externí odkaz:
http://arxiv.org/abs/2306.15142
Multilingual text recognition (MLTR) systems typically focus on a fixed set of languages, which makes it difficult to handle newly added languages or adapt to ever-changing data distribution. In this paper, we propose the Incremental MLTR (IMLTR) tas
Externí odkaz:
http://arxiv.org/abs/2305.14758
Text irregularities pose significant challenges to scene text recognizers. Thin-Plate Spline (TPS)-based rectification is widely regarded as an effective means to deal with them. Currently, the calculation of TPS transformation parameters purely depe
Externí odkaz:
http://arxiv.org/abs/2305.05322