Zobrazeno 1 - 10
of 276
pro vyhledávání: '"Li Zejian"'
Publikováno v:
Mitochondrial DNA. Part B. Resources, Vol 8, Iss 9, Pp 1012-1015 (2023)
The complete mitochondrial genome of Pachycephus smyrnensis Stein, 1876 collected from Sivas, Turkey, is described. The circled genome is 20,393 bp in length and contains a typical set of 37 genes. The missing control regions, trnQ and trnI in previo
Externí odkaz:
https://doaj.org/article/3d64c97d52a84b359261a6f9e2fdb876
Programming has become an essential component of K-12 education and serves as a pathway for developing computational thinking skills. Given the complexity of programming and the advanced skills it requires, previous research has introduced user-frien
Externí odkaz:
http://arxiv.org/abs/2412.09001
Autor:
Li, Zejian, Meng, Chenye, Li, Yize, Yang, Ling, Zhang, Shengyuan, Ma, Jiarui, Li, Jiayi, Yang, Guang, Yang, Changyuan, Yang, Zhiyuan, Chang, Jinxiong, Sun, Lingyun
Recent advances in text-to-image (T2I) generation have shown remarkable success in producing high-quality images from text. However, existing T2I models show decayed performance in compositional image generation involving multiple objects and intrica
Externí odkaz:
http://arxiv.org/abs/2412.08580
Autor:
Zhang, Shengyuan, Zhao, An, Yang, Ling, Li, Zejian, Meng, Chenye, Xu, Haoran, Chen, Tianrun, Wei, AnYang, GU, Perry Pengyun, Sun, Lingyun
Diffusion models have been applied to 3D LiDAR scene completion due to their strong training stability and high completion quality. However, the slow sampling speed limits the practical application of diffusion-based scene completion models since aut
Externí odkaz:
http://arxiv.org/abs/2412.03515
Autor:
Delmonte, Anna, Li, Zejian, Passarelli, Gianluca, Song, Eric Yilun, Barberena, Diego, Rey, Ana Maria, Fazio, Rosario
A key challenge in observing measurement-induced phase transitions is the mitigation of the post-selection barrier, which causes the reproducibility of specific sequences of measurement readouts--the trajectory--to be exponentially small in system si
Externí odkaz:
http://arxiv.org/abs/2410.05394
Autor:
Chen, Tianrun, Yu, Chunan, Hu, Yuanqi, Li, Jing, Xu, Tao, Cao, Runlong, Zhu, Lanyun, Zang, Ying, Zhang, Yong, Li, Zejian, Sun, Linyun
In this paper, we propose Img2CAD, the first approach to our knowledge that uses 2D image inputs to generate CAD models with editable parameters. Unlike existing AI methods for 3D model generation using text or image inputs often rely on mesh-based r
Externí odkaz:
http://arxiv.org/abs/2410.03417
Autor:
Zhang, Shengyuan, Yang, Ling, Li, Zejian, Zhao, An, Meng, Chenye, Yang, Changyuan, Yang, Guang, Yang, Zhiyuan, Sun, Lingyun
Accelerating the sampling speed of diffusion models remains a significant challenge. Recent score distillation methods distill a heavy teacher model into a student generator to achieve one-step generation, which is optimized by calculating the differ
Externí odkaz:
http://arxiv.org/abs/2408.15991
Autor:
Chen, Tianrun, Lu, Ankang, Zhu, Lanyun, Ding, Chaotao, Yu, Chunan, Ji, Deyi, Li, Zejian, Sun, Lingyun, Mao, Papa, Zang, Ying
The advent of large models, also known as foundation models, has significantly transformed the AI research landscape, with models like Segment Anything (SAM) achieving notable success in diverse image segmentation scenarios. Despite its advancements,
Externí odkaz:
http://arxiv.org/abs/2408.04579
Autor:
Chen, Tianrun, Ding, Chaotao, Zhu, Lanyun, Xu, Tao, Ji, Deyi, Wang, Yan, Zang, Ying, Li, Zejian
Convolutional Neural Networks (CNNs) and Vision Transformers (ViT) have been pivotal in biomedical image segmentation, yet their ability to manage long-range dependencies remains constrained by inherent locality and computational overhead. To overcom
Externí odkaz:
http://arxiv.org/abs/2407.01530
Autor:
Chen, Tianrun, Yu, Chunan, Li, Jing, Zhang, Jianqi, Zhu, Lanyun, Ji, Deyi, Zhang, Yong, Zang, Ying, Li, Zejian, Sun, Lingyun
In this paper, we introduce a new task: Zero-Shot 3D Reasoning Segmentation for parts searching and localization for objects, which is a new paradigm to 3D segmentation that transcends limitations for previous category-specific 3D semantic segmentati
Externí odkaz:
http://arxiv.org/abs/2405.19326