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• W4386960921 startingPage "378" @default.
• W4386960921 abstract "Human motion prediction is a challenging and meaningful task in many fields. Recent work has shown that the Graphical Convolutional Network (GCN) based model is very effective for this task. However, a simple GCN cannot perfectly model dynamic information with different inputs. In addition, the blank adjacency matrix in GCN has an omnidirectional search space, making it difficult to converge. Different from existing approaches, this paper proposes a new GCN module, called Graph Convolution Module with Pattern affected Adjacency Matrix (GCN-PAAM), to enhance the adaptability of GCN to different inputs and improve the learning ability of adjacency matrix. We have applied this module to different advanced GCN based models. Experimental results on H3.6m and 3DPW datasets have shown that our module can improve the experimental accuracy of the model by 1% to 9% to varying degrees, achieving state-of-the-art performance." @default.
• W4386960921 created "2023-09-23" @default.
• W4386960921 creator A5003484876 @default.
• W4386960921 creator A5054137460 @default.
• W4386960921 date "2023-01-01" @default.
• W4386960921 modified "2023-09-29" @default.
• W4386960921 title "GCN with Pattern Affected Matrix in Human Motion Prediction" @default.
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• W4386960921 doi "https://doi.org/10.1007/978-981-99-6187-0_38" @default.
• W4386960921 hasPublicationYear "2023" @default.
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