Higher resolutions

[mediarl]

First of all, thank you for the excellent work.

I tried to infer some images with higher resolution (around 2000 x 1500) and the mask generated seems blurry on the edges.
Do you think it's because of the resolution of the images used for training ?
Do you think that by training the u-net with higher resolution images, I would get better results?
Do you know if a library similar to DUTS exist with higher resolution images?

Thank you!

[xuebinqin]

Thanks for your interests. Generally, when you feed large size image e.g. 2000x1500 to the model, it resizes the image to 320x320 and do the inference process to predict the salient object detection map and then resize the map back to 2000x1500. The boundary will be blurred in the process of upsampling. If you change the input size of the model from 320x320 to 2000x1500. There will be another issue: the model was trained on the images resized to 320x320. But, "salient" is a relative magnitude. The model performance also relies on the relative ratio between the receptive fields' size and the exact objects' scale. Therefore, the performance may also degrade in this case. Purely trying with u-net with higher resolution may not give your better results. As I mentioned above the receptive fields may also have to be changed, which will inevitably introduce large amounts of computation costs. Towards salient object detection from high resolution images, you can take a look at this paper: http://openaccess.thecvf.com/content_ICCV_2019/papers/Zeng_Towards_High-Resolution_Salient_Object_Detection_ICCV_2019_paper.pdf . According to our experiences, downsampling input images to the half of its orginal size won't degrade much of the segmentation results (e.g. 2000x1500->1000x750). Further downsampling will introduce more errors. Actually, high resolution salient object detection is a very challenging yet important task. We are also planning to explore more in this area. Best of luck.

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On Sat, May 16, 2020 at 4:07 PM mediarl ***@***.***> wrote: First of all, thank you for the excellent work. I tried to infer some images with higher resolution (around 2000 x 1500) and the mask generated seems blurry on the edges. Do you think it's because of the resolution of the images used for training ? Do you think that by training the u-net with higher resolution images, I would get better results? Do you know if a library similar to DUTS exist with higher resolution images? Thank you! — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#19>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ADSGORN43IXZBZO2ZXMFX5LRR4FDHANCNFSM4NDDCF4Q> .

-- Xuebin Qin PhD Department of Computing Science University of Alberta, Edmonton, AB, Canada Homepage:https://webdocs.cs.ualberta.ca/~xuebin/