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of 3 693
pro vyhledávání: '"li, Ao"'
Camouflaged Object Segmentation (COS) faces significant challenges due to the scarcity of annotated data, where meticulous pixel-level annotation is both labor-intensive and costly, primarily due to the intricate object-background boundaries. Address
Externí odkaz:
http://arxiv.org/abs/2410.16953
Through the collaboration of multiple agents possessing diverse expertise and tools, multi-agent systems achieve impressive progress in solving real-world problems. Given the user queries, the meta-agents, serving as the brain within these systems, a
Externí odkaz:
http://arxiv.org/abs/2410.02189
Platooning technology is renowned for its precise vehicle control, traffic flow optimization, and energy efficiency enhancement. However, in large-scale mixed platoons, vehicle heterogeneity and unpredictable traffic conditions lead to virtual bottle
Externí odkaz:
http://arxiv.org/abs/2408.07578
We theoretically demonstrate a spontaneous spin superconductor (SC) state in ABCA-stacked tetralayer graphene, under sequential effects of electron-electron (e-e) and electron-hole (e-h) interactions. First of all, we examine the ferromagnetic (FM) e
Externí odkaz:
http://arxiv.org/abs/2407.19973
Autor:
Wang, Tianyu, Li, Sheng, Li, Bingyao, Dai, Yue, Li, Ao, Yuan, Geng, Ding, Yufei, Zhang, Youtao, Tang, Xulong
Continuous learning (CL) has emerged as one of the most popular deep learning paradigms deployed in modern cloud GPUs. Specifically, CL has the capability to continuously update the model parameters (through model retraining) and use the updated mode
Externí odkaz:
http://arxiv.org/abs/2407.13126
High-level synthesis (HLS) has significantly advanced the automation of digital circuits design, yet the need for expertise and time in pragma tuning remains challenging. Existing solutions for the design space exploration (DSE) adopt either heuristi
Externí odkaz:
http://arxiv.org/abs/2407.08797
Image enhancement holds extensive applications in real-world scenarios due to complex environments and limitations of imaging devices. Conventional methods are often constrained by their tailored models, resulting in diminished robustness when confro
Externí odkaz:
http://arxiv.org/abs/2406.00508
Autor:
Zheng, Zhuonan, Zhou, Sheng, Xu, Hongjia, Gu, Ming, Xu, Yilun, Li, Ao, Li, Yuhong, Gu, Jingjun, Bu, Jiajun
Graph Neural Networks (GNNs) have achieved remarkable success in various graph mining tasks by aggregating information from neighborhoods for representation learning. The success relies on the homophily assumption that nearby nodes exhibit similar be
Externí odkaz:
http://arxiv.org/abs/2405.20640
Autor:
Liu, Weizhen, Tan, Jiayu, Lan, Guangyu, Li, Ao, Li, Dongye, Zhao, Le, Yuan, Xiaohui, Dong, Nanqing
Accurate phenotypic analysis in aquaculture breeding necessitates the quantification of subtle morphological phenotypes. Existing datasets suffer from limitations such as small scale, limited species coverage, and inadequate annotation of keypoints f
Externí odkaz:
http://arxiv.org/abs/2405.12476
Autor:
Liu, Weizhen, Li, Ao, Wu, Ze, Li, Yue, Ge, Baobin, Lan, Guangyu, Chen, Shilin, Li, Minghe, Liu, Yunfei, Yuan, Xiaohui, Dong, Nanqing
Hierarchical leaf vein segmentation is a crucial but under-explored task in agricultural sciences, where analysis of the hierarchical structure of plant leaf venation can contribute to plant breeding. While current segmentation techniques rely on dat
Externí odkaz:
http://arxiv.org/abs/2405.10041