Zobrazeno 1 - 10
of 915
pro vyhledávání: '"Bai Yue"'
Autor:
BAI Yue-lei, YIN Hang, SONG Guang-ping, HE Xiao-dong, QI Xin-xin, GAO Jin, HAO Bing-bing, ZHANG Jin-ze
Publikováno v:
Cailiao gongcheng, Vol 49, Iss 5, Pp 1-23 (2021)
The MAX phase of the ternary layered compound and the recently attracted attention of the MAB phase have become the research hotspots in the field of structural ceramics for more than 20 years because of their common characteristics of ceramics and m
Externí odkaz:
https://doaj.org/article/73d62719f76a44cc9507292183660c33
Vision foundation models are renowned for their generalization ability due to massive training data. Nevertheless, they demand tremendous training resources, and the training data is often inaccessible, e.g., CLIP, DINOv2, posing great challenges to
Externí odkaz:
http://arxiv.org/abs/2407.10366
Training large language models (LLMs) and multimodal LLMs necessitates significant computing resources, and existing publicly available LLMs are typically pre-trained on diverse, privately curated datasets spanning various tasks. For instance, LLaMA,
Externí odkaz:
http://arxiv.org/abs/2407.08196
Recent studies have drawn attention to the untapped potential of the "star operation" (element-wise multiplication) in network design. While intuitive explanations abound, the foundational rationale behind its application remains largely unexplored.
Externí odkaz:
http://arxiv.org/abs/2403.19967
Current training pipelines in object recognition neglect Hue Jittering when doing data augmentation as it not only brings appearance changes that are detrimental to classification, but also the implementation is inefficient in practice. In this study
Externí odkaz:
http://arxiv.org/abs/2403.09506
Latent graph inference (LGI) aims to jointly learn the underlying graph structure and node representations from data features. However, existing LGI methods commonly suffer from the issue of supervision starvation, where massive edge weights are lear
Externí odkaz:
http://arxiv.org/abs/2310.04314
Existing video recognition algorithms always conduct different training pipelines for inputs with different frame numbers, which requires repetitive training operations and multiplying storage costs. If we evaluate the model using other frames which
Externí odkaz:
http://arxiv.org/abs/2303.14817
Anomaly detection in videos is a significant yet challenging problem. Previous approaches based on deep neural networks employ either reconstruction-based or prediction-based approaches. Nevertheless, existing reconstruction-based methods 1) rely on
Externí odkaz:
http://arxiv.org/abs/2301.12048