Image Blending-assisted Few-Shot Cross-Domain Similarity Learning and Adaptation Tasks for Ambiguous Hazardous Incidents

Nowadays, machine learning technology is growing exponentially, our research integrates AI and digital twin and/or transformation implemented in a wide range of industries. The issues can be seen in the fields which is the use of knowledge from both the virtual and physical world adapted all the way through. Part of obvious issue seems similar to deep transfer learning, such a domain shift occurrence during two domains. Hence, our proposed framework was developed to learn domain-invariant representation through Kernel Higher-order Tensor Matching (KHoM) and emphasized by cross-domain similarity learning via SoftTriple. Results, where are evaluated on public dataset and new fatal circumstance data, have been investigated that our framework is able to diminish discrepant domains from transferable on higher-level feature domain-invariant lightly on a less exemplary adaptations, but be obtained tremendously by the backing of the recognizability and realizing of object homogeneity through learning the likeness.