Zobrazeno 1 - 10
of 32 202
pro vyhledávání: '"WANG, YUAN"'
Adverse weather removal aims to restore clear vision under adverse weather conditions. Existing methods are mostly tailored for specific weather types and rely heavily on extensive labeled data. In dealing with these two limitations, this paper prese
Externí odkaz:
http://arxiv.org/abs/2409.19679
Autor:
Qin, Ying, Zhu, Jin-Ping, Meynet, Georges, Zhang, Bing, Wang, Fa-Yin, Shu, Xin-Wen, Song, Han-Feng, Wang, Yuan-Zhu, Yuan, Liang, Wang, Zhen-Han-Tao, Hu, Rui-Chong, Wu, Dong-Hong, Yi, Shuang-Xi, Tang, Qing-Wen, Wei, Jun-Jie, Wu, Xue-Feng, Liang, En-Wei
On April 25th, 2019, the LIGO-Virgo Collaboration discovered a Gravitational-wave (GW) signal from a binary neutron star (BNS) merger, i.e., GW190425. Due to the inferred large total mass, the origin of GW190425 remains unclear. We perform detailed s
Externí odkaz:
http://arxiv.org/abs/2409.10869
Ads Content Safety at Google requires classifying billions of ads for Google Ads content policies. Consistent and accurate policy enforcement is important for advertiser experience and user safety and it is a challenging problem, so there is a lot of
Externí odkaz:
http://arxiv.org/abs/2409.15343
Point cloud few-shot semantic segmentation (PC-FSS) aims to segment targets of novel categories in a given query point cloud with only a few annotated support samples. The current top-performing prototypical learning methods employ prototypes origina
Externí odkaz:
http://arxiv.org/abs/2408.13752
Autor:
Wang, Yuan, Li, Ming, Gao, Mingyi, Zou, Chang-Ling, Dong, Chun-Hua, Yang, Xiaoniu, Xuan, Qi, Ren, HongLiang
On-chip micro-ring resonators (MRRs) have been proposed for constructing delay reservoir computing (RC) systems, offering a highly scalable, high-density computational architecture that is easy to manufacture. However, most proposed RC schemes have u
Externí odkaz:
http://arxiv.org/abs/2408.13476
Recently, applying neural networks to address combinatorial optimization problems (COPs) has attracted considerable research attention. The prevailing methods always train deep models independently on specific problems, lacking a unified framework fo
Externí odkaz:
http://arxiv.org/abs/2408.12214
Mixture-of-Experts (MoE) models are designed to enhance the efficiency of large language models (LLMs) without proportionally increasing the computational demands. However, their deployment on edge devices still faces significant challenges due to hi
Externí odkaz:
http://arxiv.org/abs/2408.10284
Missing data in tabular dataset is a common issue as the performance of downstream tasks usually depends on the completeness of the training dataset. Previous missing data imputation methods focus on numeric and categorical columns, but we propose a
Externí odkaz:
http://arxiv.org/abs/2408.02128
Class-incremental learning (CIL) thrives due to its success in processing the influx of information by learning from continuously added new classes while preventing catastrophic forgetting about the old ones. It is essential for the performance break
Externí odkaz:
http://arxiv.org/abs/2408.01356
Self-similarity techniques are booming in blind super-resolution (SR) due to accurate estimation of the degradation types involved in low-resolution images. However, high-dimensional matrix multiplication within self-similarity computation prohibitiv
Externí odkaz:
http://arxiv.org/abs/2408.00470