Addressing issues regarding this system about deepdanbooru HOT 3 OPEN

Some useful information regarding the semantic segmentation of images https://github.com/mrgloom/awesome-semantic-segmentation
Weird problems that will arise from using the repos within verbatim:

1. How do we deal with tag synonyms and tag subsets? Do we create a system of which segmented regions can have multiple tags?
2. What about character tags vs facial/clothing component tags? How do we correlate them together into a logical manner? hierarchies?
3. What about segmented regions that are too small? Would it get picked up by DD 1.0 but not DD+SS system?
4. How many layers do we need maximum? 32? (since that is the maximum amount of tags per image in general?) 64/128/256?

from deepdanbooru.

Some ideas in how to implement a Semantic Segmentation dataset/model "ShoujoSegment"

1. The initial dataset phase

• Gather a list of images with strong heatmap confidence
• Use Recaptha's 3x3 5F-3T-1U test to refine the borders (remember to augment and noise them)
• Collect results from volunteers and address weighting and credibility issues

2. The Semantic Segmentation training phase

• Create the system model (or better yet multiple models)
• Use the collected data to train the system
• Optimize the system speed and accuracy wise regarding ensembles

3. The data refinement phase

• Increase the scope of images used
• Use Recaptha's 3x3 5F-3T-1U test to refine the borders (remember to augment and noise them)
• Use volunteer's results to refine the Semantic Segmentation

4. Others things that can be done outside of this loop

• Create micro-models (that is a simplified version of the main model) for mobile systems
• Apply this system into a new social media network for community contributions
• Use the "ShoujoSegment" system to refine DeepDanbooru and vice versa

This concept would be applied as the "Humans in the Loop"or "Active Learning" system.
A good example would be:

• https://github.com/marc-gorriz/CEAL-Medical-Image-Segmentation and https://arxiv.org/abs/1711.09168
• https://github.com/sinhasam/vaal and https://arxiv.org/abs/1904.00370
• https://github.com/janelia-flyem/gala
• https://github.com/nihalsid/ViewAL and https://arxiv.org/abs/1911.11789
• https://github.com/SarderLab/H-AI-L and https://arxiv.org/abs/1812.07509
• https://github.com/neuropoly/deep-active-learning
• https://github.com/Sheshansh/SuggestiveAnnotation and https://arxiv.org/abs/1706.04737

If there are crowdsourced Semantic Segmentation this can help http://ilpubs.stanford.edu:8090/1161/1/main.pdf and http://ceur-ws.org/Vol-2173/paper10.pdf

from deepdanbooru.

I am just going to put this here, for those who wants to go from label to table.

• https://people.csail.mit.edu/mrub/ObjectDiscovery/
• https://www.researchgate.net/publication/325052999_Object_Discovery_and_Cosegmentation_Based_on_Dense_Correspondences
• https://www.researchgate.net/publication/320350075_Unsupervised_Image_Co-segmentation_via_Guidance_of_Simple_Images

from deepdanbooru.