weakness of the paper is the experimental evaluation. The only experimental results are reported on synthetic datasets, i.e. MNIST and MNIST multi-set. As the objects are quite salient on the black background, it is difficult to judge the advantage of the proposed attention mechanism. In natural images, as discussed in the limitations, detecting saliency with high confidence is an issue. However, this work having been motivated partially as a framework for an improvement to existing saliency models should have been evaluated in more realistic scenarios. Furthermore, the proposed attention model is not compared with any existing attention models. Also, a comparison with human gaze based as attention (as discussed in the introduction) would be interesting. A candidate dataset is CUB annotated with human gaze data in Karessli et.al, Gaze Embeddings for Zero-Shot Image Classification, CVPR17 (another un-cited related work) which showed that human gaze based visual attention is class-specific.
Minor comment:
- The references list contains duplicates and the publication venues and/or the publication years of many of the papers are missing.
- The references list contains duplicates and the publication venues and/or the publication years of many of the papers are missing.