Zobrazeno 1 - 10
of 461
pro vyhledávání: '"Ye, Yunming"'
Autor:
Liang, Guotao, Zhang, Baoquan, Wang, Yaowei, Li, Xutao, Ye, Yunming, Wang, Huaibin, Luo, Chuyao, Ye, Kola, Luo, linfeng
Vector quantization (VQ) is a key technique in high-resolution and high-fidelity image synthesis, which aims to learn a codebook to encode an image with a sequence of discrete codes and then generate an image in an auto-regression manner. Although ex
Externí odkaz:
http://arxiv.org/abs/2405.14206
The accurate detection of Mesoscale Convective Systems (MCS) is crucial for meteorological monitoring due to their potential to cause significant destruction through severe weather phenomena such as hail, thunderstorms, and heavy rainfall. However, t
Externí odkaz:
http://arxiv.org/abs/2404.17186
Convection (thunderstorm) develops rapidly within hours and is highly destructive, posing a significant challenge for nowcasting and resulting in substantial losses to nature and society. After the emergence of artificial intelligence (AI)-based meth
Externí odkaz:
http://arxiv.org/abs/2404.10512
Autor:
Zhang, Baoquan, Wang, Huaibin, Chuyao, Luo, Li, Xutao, Guotao, Liang, Ye, Yunming, Qi, Xiaochen, He, Yao
Vector-Quantized Image Modeling (VQIM) is a fundamental research problem in image synthesis, which aims to represent an image with a discrete token sequence. Existing studies effectively address this problem by learning a discrete codebook from scrat
Externí odkaz:
http://arxiv.org/abs/2403.10071
Autor:
Yu, Demin, Li, Xutao, Ye, Yunming, Zhang, Baoquan, Luo, Chuyao, Dai, Kuai, Wang, Rui, Chen, Xunlai
Precipitation nowcasting is an important spatio-temporal prediction task to predict the radar echoes sequences based on current observations, which can serve both meteorological science and smart city applications. Due to the chaotic evolution nature
Externí odkaz:
http://arxiv.org/abs/2312.06734
Online continual learning (OCL) aims to continuously learn new data from a single pass over the online data stream. It generally suffers from the catastrophic forgetting issue. Existing replay-based methods effectively alleviate this issue by replayi
Externí odkaz:
http://arxiv.org/abs/2309.15038
Online continual learning aims to continuously train neural networks from a continuous data stream with a single pass-through data. As the most effective approach, the rehearsal-based methods replay part of previous data. Commonly used predictors in
Externí odkaz:
http://arxiv.org/abs/2309.04081
Equipping a deep model the abaility of few-shot learning, i.e., learning quickly from only few examples, is a core challenge for artificial intelligence. Gradient-based meta-learning approaches effectively address the challenge by learning how to lea
Externí odkaz:
http://arxiv.org/abs/2307.16424
Publikováno v:
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023
Online class-incremental continual learning is a specific task of continual learning. It aims to continuously learn new classes from data stream and the samples of data stream are seen only once, which suffers from the catastrophic forgetting issue,
Externí odkaz:
http://arxiv.org/abs/2304.04408
Few-Shot Learning (FSL) is a challenging task, which aims to recognize novel classes with few examples. Recently, lots of methods have been proposed from the perspective of meta-learning and representation learning. However, few works focus on the in
Externí odkaz:
http://arxiv.org/abs/2203.01482