Zobrazeno 1 - 10
of 2 857
pro vyhledávání: '"CHEN Jiahui"'
The discovery of new materials is very important to the field of materials science. When researchers explore new materials, they often have expected performance requirements for their crystal structure. In recent years, data-driven methods have made
Externí odkaz:
http://arxiv.org/abs/2411.08464
Autor:
Yang, Huan, Chen, Jiahui, Ding, Chaofan, Shi, Runhua, Xiong, Siyu, Hong, Qingqi, Mo, Xiaoqi, Di, Xinhan
Gestures are pivotal in enhancing co-speech communication. While recent works have mostly focused on point-level motion transformation or fully supervised motion representations through data-driven approaches, we explore the representation of gesture
Externí odkaz:
http://arxiv.org/abs/2409.17674
Autor:
Chen, Qiang, Su, Xiangbo, Zhang, Xinyu, Wang, Jian, Chen, Jiahui, Shen, Yunpeng, Han, Chuchu, Chen, Ziliang, Xu, Weixiang, Li, Fanrong, Zhang, Shan, Yao, Kun, Ding, Errui, Zhang, Gang, Wang, Jingdong
In this paper, we present a light-weight detection transformer, LW-DETR, which outperforms YOLOs for real-time object detection. The architecture is a simple stack of a ViT encoder, a projector, and a shallow DETR decoder. Our approach leverages rece
Externí odkaz:
http://arxiv.org/abs/2406.03459
Despite the prevalence of reconstruction-based deep learning methods, time series anomaly detection remains challenging. Existing approaches often struggle with limited temporal contexts, inadequate representation of normal patterns, and flawed evalu
Externí odkaz:
http://arxiv.org/abs/2405.11238
Autor:
Chen, Dong, Liu, Gengzhuo, Du, Hongyan, Wee, Junjie, Wang, Rui, Chen, Jiahui, Shen, Jana, Wei, Guo-Wei
As COVID-19 enters its fifth year, it continues to pose a significant global health threat, with the constantly mutating SARS-CoV-2 virus challenging drug effectiveness. A comprehensive understanding of virus-drug interactions is essential for predic
Externí odkaz:
http://arxiv.org/abs/2403.02603
Autor:
Sun, Yanpeng, Chen, Jiahui, Zhang, Shan, Zhang, Xinyu, Chen, Qiang, Zhang, Gang, Ding, Errui, Wang, Jingdong, Li, Zechao
In this paper, we propose a novel Visual Reference Prompt (VRP) encoder that empowers the Segment Anything Model (SAM) to utilize annotated reference images as prompts for segmentation, creating the VRP-SAM model. In essence, VRP-SAM can utilize anno
Externí odkaz:
http://arxiv.org/abs/2402.17726
Autor:
Zhang, Ruochi, Wu, Haoran, Liu, Chang, Li, Huaping, Wu, Yuqian, Li, Kewei, Wang, Yifan, Deng, Yifan, Chen, Jiahui, Zhou, Fengfeng, Gao, Xin
Recent advances in protein language models have catalyzed significant progress in peptide sequence representation. Despite extensive exploration in this field, pre-trained models tailored for peptide-specific needs remain largely unaddressed due to t
Externí odkaz:
http://arxiv.org/abs/2401.11360
Electrostatics is of paramount importance to chemistry, physics, biology, and medicine. The Poisson-Boltzmann (PB) theory is a primary model for electrostatic analysis. However, it is highly challenging to compute accurate PB electrostatic solvation
Externí odkaz:
http://arxiv.org/abs/2312.11482
Autor:
Sinha, Animesh, Sun, Bo, Kalia, Anmol, Casanova, Arantxa, Blanchard, Elliot, Yan, David, Zhang, Winnie, Nelli, Tony, Chen, Jiahui, Shah, Hardik, Yu, Licheng, Singh, Mitesh Kumar, Ramchandani, Ankit, Sanjabi, Maziar, Gupta, Sonal, Bearman, Amy, Mahajan, Dhruv
We introduce Style Tailoring, a recipe to finetune Latent Diffusion Models (LDMs) in a distinct domain with high visual quality, prompt alignment and scene diversity. We choose sticker image generation as the target domain, as the images significantl
Externí odkaz:
http://arxiv.org/abs/2311.10794
Protein mutations can significantly influence protein solubility, which results in altered protein functions and leads to various diseases. Despite of tremendous effort, machine learning prediction of protein solubility changes upon mutation remains
Externí odkaz:
http://arxiv.org/abs/2310.18760