Zobrazeno 1 - 10
of 1 580
pro vyhledávání: '"Zhu Wentao"'
Achieving realistic animated human avatars requires accurate modeling of pose-dependent clothing deformations. Existing learning-based methods heavily rely on the Linear Blend Skinning (LBS) of minimally-clothed human models like SMPL to model deform
Externí odkaz:
http://arxiv.org/abs/2411.19942
Dynamic positron emission tomography (PET) images can reveal the distribution of tracers in the organism and the dynamic processes involved in biochemical reactions, and it is widely used in clinical practice. Despite the high effectiveness of dynami
Externí odkaz:
http://arxiv.org/abs/2410.22674
Publikováno v:
Redai dili, Vol 41, Iss 1, Pp 114-123 (2021)
Agglomeration is an important feature of the spatial distribution of an urban internal service industry. Most of the previous studies on the influencing factors of urban internal service industry agglomeration have ignored traffic factors, especially
Externí odkaz:
https://doaj.org/article/f710b1582c6b4813abae20d1b1d02040
Autor:
Jin, Yuan, Ma, Gege, Chen, Geng, Lyu, Tianling, Egger, Jan, Lyu, Junhui, Zhang, Shaoting, Zhu, Wentao
The accurate diagnosis of pathological subtypes of lung cancer is of paramount importance for follow-up treatments and prognosis managements. Assessment methods utilizing deep learning technologies have introduced novel approaches for clinical diagno
Externí odkaz:
http://arxiv.org/abs/2407.13092
Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks
Graph neural networks (GNNs) are widely applied in graph data modeling. However, existing GNNs are often trained in a task-driven manner that fails to fully capture the intrinsic nature of the graph structure, resulting in sub-optimal node and graph
Externí odkaz:
http://arxiv.org/abs/2407.11361
Despite the Graph Neural Networks' (GNNs) proficiency in analyzing graph data, achieving high-accuracy and interpretable predictions remains challenging. Existing GNN interpreters typically provide post-hoc explanations disjointed from GNNs' predicti
Externí odkaz:
http://arxiv.org/abs/2407.11358
Human motion generation is a critical task with a wide range of applications. Achieving high realism in generated motions requires naturalness, smoothness, and plausibility. Despite rapid advancements in the field, current generation methods often fa
Externí odkaz:
http://arxiv.org/abs/2407.02272
Recently, video object segmentation (VOS) networks typically use memory-based methods: for each query frame, the mask is predicted by space-time matching to memory frames. Despite these methods having superior performance, they suffer from two issues
Externí odkaz:
http://arxiv.org/abs/2405.04042
Temporal repetition counting aims to quantify the repeated action cycles within a video. The majority of existing methods rely on the similarity correlation matrix to characterize the repetitiveness of actions, but their scalability is hindered due t
Externí odkaz:
http://arxiv.org/abs/2403.01543
Understanding and attributing mental states, known as Theory of Mind (ToM), emerges as a fundamental capability for human social reasoning. While Large Language Models (LLMs) appear to possess certain ToM abilities, the mechanisms underlying these ca
Externí odkaz:
http://arxiv.org/abs/2402.18496