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The problem of image change detection via everyday indoor robot navigation isexplored from a novel perspective of the self-attention technique. Detectingsemantically non-distinctive and visually small changes remains a key challengein the robotics community. Intuitively, these small non-distinctive changes maybe better handled by the recent paradigm of the attention mechanism, which isthe basic idea of this work. However, existing self-attention models requiresignificant retraining cost per domain, so it is not directly applicable torobotics applications. We propose a new self-attention technique with anability of unsupervised on-the-fly domain adaptation, which introduces anattention mask into the intermediate layer of an image change detection model,without modifying the input and output layers of the model. Experiments, inwhich an indoor robot aims to detect visually small changes in everydaynavigation, demonstrate that our attention technique significantly boosts thestate-of-the-art image change detection model.

Domain Invariant Siamese Attention Mask for Small Object Change Detection via Everyday Indoor Robot Navigation