swdsld
swdsld OP t1_ixou56r wrote
Reply to comment by boyetosekuji in [Project] Background removal tool based on our recent work "Revisiting Image Pyramid Structure for High Resolution Salient Object Detection" by swdsld
Thank you for letting me know about the problem. It's actually quite common in those kind of background removal tools. When the model is not sure whether the region is a part of salient object or not, it produces such artifacts. It could happen for both our method and other companies' tool. We can avoid that problem by training the model with various scenes which is what I'm planning to in the future release.
Tools like remove.bg or Apple's native tool are provided commercially, so they must have been using massive dataset including their privately annotated ground truths. We don't think that we can outperform in every scenario than theirs, but I'm trying my best training my method with the datasets which are available for me now.
Stay tuned for the future updates!
swdsld OP t1_ixnicaw wrote
Reply to comment by boyetosekuji in [Project] Background removal tool based on our recent work "Revisiting Image Pyramid Structure for High Resolution Salient Object Detection" by swdsld
We also have some problem with detecting all object in the given image. It is quite common problem for salient object detection because objects in the the dataset are usually center-biased. It could be our next research topic!
swdsld OP t1_ixngmf0 wrote
Reply to comment by fl0o0ps in [Project] Background removal tool based on our recent work "Revisiting Image Pyramid Structure for High Resolution Salient Object Detection" by swdsld
Salient object detection including my method using deep learning outputs pixel-wise binary classification result.
swdsld OP t1_ixnfsk0 wrote
Reply to comment by Due-Philosopher-1426 in [Project] Background removal tool based on our recent work "Revisiting Image Pyramid Structure for High Resolution Salient Object Detection" by swdsld
We use MIT licence which means you can use our code freely as long as all copies of the software or its substantial portions include a copy of the terms of the MIT License and also a copyright notice. Please check the Licence part of our repository. Thanks!
swdsld OP t1_ixnf5at wrote
Reply to comment by boyetosekuji in [Project] Background removal tool based on our recent work "Revisiting Image Pyramid Structure for High Resolution Salient Object Detection" by swdsld
Thank you very much for noticing!
swdsld OP t1_ixjolp8 wrote
Reply to comment by fl0o0ps in [Project] Background removal tool based on our recent work "Revisiting Image Pyramid Structure for High Resolution Salient Object Detection" by swdsld
Thank you for your attention! That's right. Previous deep learning based segmentation models already used their pyramid structure and we improved this structure for high resolution scenario.
swdsld OP t1_ixjnqnl wrote
Reply to comment by earthsworld in [Project] Background removal tool based on our recent work "Revisiting Image Pyramid Structure for High Resolution Salient Object Detection" by swdsld
Thank you for your opinion! However, can you check the results once more? Our result which is the last one clearly shows better result by corretly segmenting the propeller and the landing gear parts while apple's result ignores the propeller and shows many white pixels around the landing gear.
Also, while we agree that in other cases, Apple's tool might show better results compared to us, but I just wanted to share my work which can work better in some cases with relatively less training images (I guess) compared to the companys' official tool like Apple's.
swdsld OP t1_ixhyhlo wrote
Reply to comment by ajt9000 in [Project] Background removal tool based on our recent work "Revisiting Image Pyramid Structure for High Resolution Salient Object Detection" by swdsld
Thank you very much!
swdsld OP t1_ixoxfyf wrote
Reply to comment by boyetosekuji in [Project] Background removal tool based on our recent work "Revisiting Image Pyramid Structure for High Resolution Salient Object Detection" by swdsld
Using depth map would definitely be a great option. Thank you for your kind comment!