reviews: 
Comments: The paper considers the difficult problem of dealing with glass and mirror objects in images. 
It demonstrates, based on a newly introduced metric, the poor performance of representations from standard vision foundation models, in particular in the form of the one-shot segmentation method Matcher, when evaluated specifically on such data. The paper then proposes a scheme to select, in a supervised manner based on a few images, feature dimensions that are most affected by the addition/removal of the glass. By 'correcting' these dimensions before applying the Matcher algorithm, they show a small improvement in the results on three datasets.
Strengths: - The paper tackles a challenging open problem in visual understanding. 
- A new metric is introduced.
- The core idea of selecting feature dimensions related to the effect of glass or mirrors has some potential, but should be worked out in a more clever way.
Weaknesses: - The paper is badly structured and, as a consequence, hard to follow. Figures are not referred to in the text. Text has wrong references to tables (e.g. l. 199 refers to Table 2, that has mIOU results while text discusses RA results). There are a lot of forward references, e.g. section3 on problem analysis discusses results of tables from section 5 on experimental results, without telling the reader what data is used or what the exact setup is. 

- Overall, the above point makes it hard to know precisely what the authors did exactly. I had to make some guesses at several points. Most importantly, I'm still not sure about the actual task they are performing / evaluating. In some parts it's suggested this is about  segmentation of glass/mirror objects. But in other places it's about matching between a reference image and a target image.  I assume what the authors did in the end is close to the one-shot segmentation of Matcher. 

- The method is not described rigorously, making it impossible to reproduce the results. In particular, the 'most' function in eq. 4 is only vaguely described. The text refers to the appendix for more details, but their only numerical values are given, still not explaining the precise algorithm. 

- The reported results are anecdotal. Results are reported only for 3-4 images per dataset. It's unclear which images these are and how they were selected.  At the very least, averaged results over the entire dataset should be reported. 

- Results show only a minor improvement (in the range of 1 or 2 %) over the very poor results of the baseline. Results are reported for one set of (manually selected?) training images. No details on how these images were selected are given. At the very least, the results should have been repeated for different sets of training images, so the standard deviation on the results could be added to the tables and the reader could get an idea on whether these results are significant or not.

- Given that a new metric is used, more naive baselines should be added: what RA values would one get with a random representation ? Are the numbers reported significantly better ? 

- The whole paper builds on one baseline work, Matcher. The proposed method is applied only on top of that method and the results are only compared against that method.  Other state-of-the-art methods, or extra baselines, should be added to the comparison. There is no further analysis, such as a sensitivity analysis of the hyperparameters used, an ablation study or comparison of different variants of the method (e.g. determining the lambda parameter for each of the selected dimensions separately, based on the observed differences in the training data). There are no qualitative results included neither.
Recommendation: 3
Reviewer confidence: 4
Soundness/correctness: 2
Clarity: 1