Zobrazeno 1 - 10
of 7 579
pro vyhledávání: '"Yanchun P"'
Recently, 3D backdoor attacks have posed a substantial threat to 3D Deep Neural Networks (3D DNNs) designed for 3D point clouds, which are extensively deployed in various security-critical applications. Although the existing 3D backdoor attacks achie
Externí odkaz:
http://arxiv.org/abs/2412.07511
Autor:
Yang, Guochao, Zhao, Jingkun, Liang, Yanchun, Spite, Monique, Spite, Francois, Shi, Jianrong, Liu, Shuai, Liu, Nian, Cui, Wenyuan, Zhao, Gang
Based on the high resolution and high signal-to-noise spectra, we derived the chemical abundances of 20 elements for 20 barium (Ba-) stars. For the first time, the detailed abundances of four sample stars, namely HD 92482, HD 150430, HD 151101 and HD
Externí odkaz:
http://arxiv.org/abs/2410.13177
Autor:
Liu, Yonghao, Li, Mengyu, Li, Ximing, Huang, Lan, Giunchiglia, Fausto, Liang, Yanchun, Feng, Xiaoyue, Guan, Renchu
Node classification is an essential problem in graph learning. However, many models typically obtain unsatisfactory performance when applied to few-shot scenarios. Some studies have attempted to combine meta-learning with graph neural networks to sol
Externí odkaz:
http://arxiv.org/abs/2407.14732
Autor:
Feng, Juexiao, Yang, Yuhong, Xie, Yanchun, Li, Yaqian, Guo, Yandong, Guo, Yuchen, He, Yuwei, Xiang, Liuyu, Ding, Guiguang
In recent years, object detection in deep learning has experienced rapid development. However, most existing object detection models perform well only on closed-set datasets, ignoring a large number of potential objects whose categories are not defin
Externí odkaz:
http://arxiv.org/abs/2402.18821
Autor:
Zhao, Jinyu, Cai, Shu, Chen, Yiwen, Gu, Genda, Yan, Hongtao, Guo, Jing, Han, Jinyu, Wang, Pengyu, Zhou, Yazhou, Li, Yanchun, Li, Xiaodong, Ren, Zhian, Wu, Qi, Zhou, Xingjiang, Ding, Yang, Xiang, Tao, Mao, Ho-kwang, Sun, Liling
Publikováno v:
Chinese Phys. Lett. 41(2024)047401
What factors fundamentally determine the value of superconducting transition temperature (Tc) in high temperature superconductors has been the subject of intense debate. Following the establishment of an empirical law known as Homes'law, there is a g
Externí odkaz:
http://arxiv.org/abs/2402.17315
Large Language Models (LLMs) represent an advanced evolution of earlier, simpler language models. They boast enhanced abilities to handle complex language patterns and generate coherent text, images, audios, and videos. Furthermore, they can be fine-
Externí odkaz:
http://arxiv.org/abs/2402.17970
Autor:
Song, Yinghao, Cao, Zhiyuan, Xiang, Wanhong, Long, Sifan, Yang, Bo, Ge, Hongwei, Liang, Yanchun, Wu, Chunguo
Low-light image enhancement (LLIE) restores the color and brightness of underexposed images. Supervised methods suffer from high costs in collecting low/normal-light image pairs. Unsupervised methods invest substantial effort in crafting complex loss
Externí odkaz:
http://arxiv.org/abs/2402.04584
Publikováno v:
IEEE Robot. Autom. Mag. 31.2( 2024)54-65
Torch relay is an important tradition of the Olympics and heralds the start of the Games. Robots applied in the torch relay activity can not only demonstrate the technological capability of humans to the world but also provide a sight of human lives
Externí odkaz:
http://arxiv.org/abs/2401.03903
Autor:
Xu, Jinjin, Xu, Liwu, Yang, Yuzhe, Li, Xiang, Wang, Fanyi, Xie, Yanchun, Huang, Yi-Jie, Li, Yaqian
Recent advancements in multi-modal large language models (MLLMs) have led to substantial improvements in visual understanding, primarily driven by sophisticated modality alignment strategies. However, predominant approaches prioritize global or regio
Externí odkaz:
http://arxiv.org/abs/2311.05348
Autor:
Huang, Xinyu, Huang, Yi-Jie, Zhang, Youcai, Tian, Weiwei, Feng, Rui, Zhang, Yuejie, Xie, Yanchun, Li, Yaqian, Zhang, Lei
In this paper, we introduce the Recognize Anything Plus Model (RAM++), an open-set image tagging model effectively leveraging multi-grained text supervision. Previous approaches (e.g., CLIP) primarily utilize global text supervision paired with image
Externí odkaz:
http://arxiv.org/abs/2310.15200