Abstract
Visual cognitive ability is important for intelligent robots in unstructured and dynamic environments. The high reliance on large amounts of data prevents prior methods to handle this task. Therefore, we propose a model called knowledge-experience fusion graph (KEFG) network for novel inference. It exploits information from both knowledge and experience. With the employment of graph convolutional network (GCN), KEFG generates the predictive classifiers of the novel classes with few labeled samples. Experiments show that KEFG can decrease the training time by the fusion of the source information and also increase the classification accuracy in cross-task learning.
This work is supported partly by National Key R&D Program of China (grants 2017YFB1300202 and 2016YFC0300801), partly by National Natural Science Foundation (NSFC) of China (grants 61973301, 61972020, 61633009, and U1613213), partly by Beijing Science and Technology Plan Project (grant Z181100008918018), and partly by Meituan Open R&D Fund.
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Zhang, X., Yang, X., Liu, Z., Zhang, L., Ren, D., Fan, M. (2021). Fusing Knowledge and Experience with Graph Convolutional Network for Cross-task Learning in Visual Cognitive Development. In: Sun, F., Liu, H., Fang, B. (eds) Cognitive Systems and Signal Processing. ICCSIP 2020. Communications in Computer and Information Science, vol 1397. Springer, Singapore. https://doi.org/10.1007/978-981-16-2336-3_10
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DOI: https://doi.org/10.1007/978-981-16-2336-3_10
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