Zobrazeno 1 - 10
of 169
pro vyhledávání: '"Lin Yiqi"'
Publikováno v:
Frontiers in Psychology, Vol 15 (2024)
ObjectiveTo explore the impact of COVID-19 on the sleep of healthcare workers from Fujian Province supporting Hubei Province and its related risk factors.MethodsA cross-sectional, anonymous, self-reported online questionnaire survey was conducted amo
Externí odkaz:
https://doaj.org/article/88cebb01317a40739d03f33b8c41f75c
Training models with longer in-context lengths is a significant challenge for multimodal model due to substantial GPU memory and computational costs. This exploratory study does not present state-of-the-art models; rather, it introduces an innovative
Externí odkaz:
http://arxiv.org/abs/2406.02547
Despite CLIP being the foundation model in numerous vision-language applications, the CLIP suffers from a severe text spotting bias. Such bias causes CLIP models to `Parrot' the visual text embedded within images while disregarding the authentic visu
Externí odkaz:
http://arxiv.org/abs/2312.14232
Publikováno v:
E3S Web of Conferences, Vol 235, p 03017 (2021)
In the era of the rapid development of information technology, the innovation of Fintech continues to send emerging research hotspots to the financial market. Based on the analysis of documents retrieved from the Web of Science database, this article
Externí odkaz:
https://doaj.org/article/0aa646eb37894a1ea6a3d1c78563b4cb
The explicit neural radiance field (NeRF) has gained considerable interest for its efficient training and fast inference capabilities, making it a promising direction such as virtual reality and gaming. In particular, PlenOctree (POT)[1], an explicit
Externí odkaz:
http://arxiv.org/abs/2307.15333
Generating and editing a 3D scene guided by natural language poses a challenge, primarily due to the complexity of specifying the positional relations and volumetric changes within the 3D space. Recent advancements in Large Language Models (LLMs) hav
Externí odkaz:
http://arxiv.org/abs/2305.15808
Recently, the self-supervised pre-training paradigm has shown great potential in leveraging large-scale unlabeled data to improve downstream task performance. However, increasing the scale of unlabeled pre-training data in real-world scenarios requir
Externí odkaz:
http://arxiv.org/abs/2212.05473
We discuss the fundamental issue of identification in linear instrumental variable (IV) models with unknown IV validity. With the assumption of the "sparsest rule", which is equivalent to the plurality rule but becomes operational in computation algo
Externí odkaz:
http://arxiv.org/abs/2207.03035
Image restoration and enhancement is a process of improving the image quality by removing degradations, such as noise, blur, and resolution degradation. Deep learning (DL) has recently been applied to image restoration and enhancement. Due to its ill
Externí odkaz:
http://arxiv.org/abs/2206.02070
Recent image matting studies are developing towards proposing trimap-free or interactive methods for complete complex image matting tasks. Although avoiding the extensive labors of trimap annotation, existing methods still suffer from two limitations
Externí odkaz:
http://arxiv.org/abs/2205.08324