Weakness: The major concern is the limited contribution of this work. 1.Using image-to-image translation to unify the representations across-domain is an existing technique in domain adaptation, especially in segmentation tasks [1,2]. 2. The use of morphologic information in this paper is simple as the combination of edge detection and segmentation, which are both employed as tools from existing benchmarks (in this paper the author used DeeplabV3, DexiNed-f, employed as off-the-shelf tools for image pre-processing purpose as mentioned in section 4). 3.There should be more on how to use the morphologic segmentation across-domain, and how morphologic segmentation should be conducted differently for different domains. Or is it exactly the same given any arbitrary domain? These questions are important given the task domain adaptation. This paper didn’t provide insight into this but assumed morphologic segmentation will be invariant. 4. Results compared to other domain adaptation methods (especially generative methods) are missing. There is an obvious lack of evidence that the proposed method is superior.
In brief, the contribution of this paper is limited, the results provided are not sufficient to support the method being effective. A reject.
[1] Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation [2] Image to Image Translation for Domain Adaptation
3.There should be more on how to use the morphologic segmentation across-domain, and how morphologic segmentation should be conducted differently for different domains. Or is it exactly the same given any arbitrary domain? These questions are important given the task domain adaptation. This paper didn’t provide insight into this but assumed morphologic segmentation will be invariant.