Zobrazeno 1 - 10
of 5 096
pro vyhledávání: '"Zhou, Sheng."'
The recently introduced Segment Anything Model (SAM), a Visual Foundation Model (VFM), has demonstrated impressive capabilities in zero-shot segmentation tasks across diverse natural image datasets. Despite its success, SAM encounters noticeably perf
Externí odkaz:
http://arxiv.org/abs/2408.12364
Autor:
Famà, Francesca, Zhou, Sheng, Heizenreder, Benedikt, Tang, Mikkel, Bennetts, Shayne, Jäger, Simon B., Schäffer, Stefan A., Schreck, Florian
Atoms coupled to cavities provide an exciting playground for the study of fundamental interactions of atoms mediated through a common channel. Many of the applications of cavity-QED and cold-atom experiments more broadly, suffer from limitations caus
Externí odkaz:
http://arxiv.org/abs/2407.18668
Autor:
Wang, Zhe, Zhou, Sheng, Chen, Jiawei, Zhang, Zhen, Hu, Binbin, Feng, Yan, Chen, Chun, Wang, Can
Learning effective representations for Continuous-Time Dynamic Graphs (CTDGs) has garnered significant research interest, largely due to its powerful capabilities in modeling complex interactions between nodes. A fundamental and crucial requirement f
Externí odkaz:
http://arxiv.org/abs/2407.16959
Unsupervised Graph Domain Adaptation (UGDA) involves the transfer of knowledge from a label-rich source graph to an unlabeled target graph under domain discrepancies. Despite the proliferation of methods designed for this emerging task, the lack of s
Externí odkaz:
http://arxiv.org/abs/2407.11052
Autor:
Sun, Qingyun, Chen, Ziying, Yang, Beining, Ji, Cheng, Fu, Xingcheng, Zhou, Sheng, Peng, Hao, Li, Jianxin, Yu, Philip S.
Graph condensation (GC) has recently garnered considerable attention due to its ability to reduce large-scale graph datasets while preserving their essential properties. The core concept of GC is to create a smaller, more manageable graph that retain
Externí odkaz:
http://arxiv.org/abs/2407.00615
Collaborative Perception (CP) has been a promising solution to address occlusions in the traffic environment by sharing sensor data among collaborative vehicles (CoV) via vehicle-to-everything (V2X) network. With limited wireless bandwidth, CP necess
Externí odkaz:
http://arxiv.org/abs/2407.00412
Leveraging the computing and sensing capabilities of vehicles, vehicular federated learning (VFL) has been applied to edge training for connected vehicles. The dynamic and interconnected nature of vehicular networks presents unique opportunities to h
Externí odkaz:
http://arxiv.org/abs/2406.17470
Autor:
Li, Haoling, Li, Changhao, Xue, Mengqi, Fang, Gongfan, Zhou, Sheng, Feng, Zunlei, Wang, Huiqiong, Wang, Yong, Cheng, Lechao, Song, Mingli, Song, Jie
Structural pruning has emerged as a promising approach for producing more efficient models. Nevertheless, the community suffers from a lack of standardized benchmarks and metrics, leaving the progress in this area not fully comprehended. To fill this
Externí odkaz:
http://arxiv.org/abs/2406.12315
Molecular property prediction (MPP) is a fundamental and crucial task in drug discovery. However, prior methods are limited by the requirement for a large number of labeled molecules and their restricted ability to generalize for unseen and new tasks
Externí odkaz:
http://arxiv.org/abs/2406.12950
Autor:
Xia, Renqiu, Mao, Song, Yan, Xiangchao, Zhou, Hongbin, Zhang, Bo, Peng, Haoyang, Pi, Jiahao, Fu, Daocheng, Wu, Wenjie, Ye, Hancheng, Feng, Shiyang, Wang, Bin, Xu, Chao, He, Conghui, Cai, Pinlong, Dou, Min, Shi, Botian, Zhou, Sheng, Wang, Yongwei, Yan, Junchi, Wu, Fei, Qiao, Yu
Scientific documents record research findings and valuable human knowledge, comprising a vast corpus of high-quality data. Leveraging multi-modality data extracted from these documents and assessing large models' abilities to handle scientific docume
Externí odkaz:
http://arxiv.org/abs/2406.11633