Using depth map would definitely be a great option. Thank you for your kind comment!

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!

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!

Salient object detection including my method using deep learning outputs pixel-wise binary classification result.

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!

Thank you very much for noticing!

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.

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.

Thank you very much!