Zobrazeno 1 - 10
of 419 919
pro vyhledávání: '"P. Bin"'
Autor:
Hsu, Aliyah R., Zhu, James, Wang, Zhichao, Bi, Bin, Mehrotra, Shubham, Pentyala, Shiva K., Tan, Katherine, Mao, Xiang-Bo, Omrani, Roshanak, Chaudhuri, Sougata, Radhakrishnan, Regunathan, Asur, Sitaram, Cheng, Claire Na, Yu, Bin
LLMs have demonstrated impressive proficiency in generating coherent and high-quality text, making them valuable across a range of text-generation tasks. However, rigorous evaluation of this generated content is crucial, as ensuring its quality remai
Externí odkaz:
http://arxiv.org/abs/2411.02448
Precise alignment of multi-modal images with inherent feature discrepancies poses a pivotal challenge in deformable image registration. Traditional learning-based approaches often consider registration networks as black boxes without interpretability
Externí odkaz:
http://arxiv.org/abs/2411.01399
Text-based person retrieval aims to identify the specific persons using textual descriptions as queries. Existing ad vanced methods typically depend on vision-language pre trained (VLP) models to facilitate effective cross-modal alignment. However, t
Externí odkaz:
http://arxiv.org/abs/2410.21318
Following the success of Large Language Models (LLMs), expanding their boundaries to new modalities represents a significant paradigm shift in multimodal understanding. Human perception is inherently multimodal, relying not only on text but also on a
Externí odkaz:
http://arxiv.org/abs/2410.18325
Existing large pre-trained models typically map text input to text output in an end-to-end manner, such as ChatGPT, or map a segment of text input to a hierarchy of action decisions, such as OpenVLA. However, humans can simultaneously generate text a
Externí odkaz:
http://arxiv.org/abs/2410.15885
Model Inversion Attacks (MIAs) aim at recovering privacy-sensitive training data from the knowledge encoded in the released machine learning models. Recent advances in the MIA field have significantly enhanced the attack performance under multiple sc
Externí odkaz:
http://arxiv.org/abs/2410.05814
The evolving field of mobile robotics has indeed increased the demand for simultaneous localization and mapping (SLAM) systems. To augment the localization accuracy and mapping efficacy of SLAM, we refined the core module of the SLAM system. Within t
Externí odkaz:
http://arxiv.org/abs/2410.05017
Autor:
Sun, Tian-Rui, Geng, Jin-Jun, Yan, Jing-Zhi, Hu, You-Dong, Wu, Xue-Feng, Castro-Tirado, Alberto J., Yang, Chao, Ping, Yi-Ding, Hu, Chen-Ran, Xu, Fan, Gao, Hao-Xuan, Jiang, Ji-An, Zhu, Yan-Tian, Xue, Yongquan, Pérez-García, Ignacio, Wu, Si-Yu, Fernández-García, Emilio, Caballero-García, María D., Sánchez-Ramírez, Rubén, Guziy, Sergiy, Olivares, Ignacio, del Pulgar, Carlos Jesus Pérez, Castellón, A., Castillo, Sebastián, Xiong, Ding-Rong, Pandey, Shashi B., Hiriart, David, García-Segura, Guillermo, Lee, William H., Carrasco-García, I. M., Park, Il H., Meintjes, Petrus J., van Heerden, Hendrik J., Martín-Carrillo, Antonio, Hanlon, Lorraine, Zhang, Bin-Bin, Maury, Alain, Hernández-García, L., Gritsevich, Maria, Rossi, Andrea, Maiorano, Elisabetta, Cusano, Felice, D'Avanzo, Paolo, Ferro, Matteo, Melandri, Andrea, De Pasquale, Massimiliano, Brivio, Riccardo, Fang, Min, Fan, Lu-Lu, Hu, Wei-Da, Wan, Zhen, Hu, Lei, Zuo, Ying-Xi, Tang, Jin-Long, Zhang, Xiao-Ling, Zheng, Xian-Zhong, Li, Bin, Luo, Wen-Tao, Liu, Wei, Wang, Jian, Zhang, Hong-Fei, Liu, Hao, Gao, Jie, Liang, Ming, Wang, Hai-Ren, Yao, Da-Zhi, Cheng, Jing-Quan, Zhao, Wen, Dai, Zi-Gao
Thanks to the rapidly increasing time-domain facilities, we are entering a golden era of research on gamma-ray bursts (GRBs). In this Letter, we report our observations of GRB 240529A with the Burst Optical Observer and Transient Exploring System, th
Externí odkaz:
http://arxiv.org/abs/2409.17983
Social media platforms have a vital role in the modern world, serving as conduits for communication, the exchange of ideas, and the establishment of networks. However, the misuse of these platforms through toxic comments, which can range from offensi
Externí odkaz:
http://arxiv.org/abs/2409.17130
Autor:
Chen, Liuhan, Li, Zongjian, Lin, Bin, Zhu, Bin, Wang, Qian, Yuan, Shenghai, Zhou, Xing, Cheng, Xinhua, Yuan, Li
Variational Autoencoder (VAE), compressing videos into latent representations, is a crucial preceding component of Latent Video Diffusion Models (LVDMs). With the same reconstruction quality, the more sufficient the VAE's compression for videos is, t
Externí odkaz:
http://arxiv.org/abs/2409.01199