Developed a point-based segmentation approach

[victor-wildlife]

Integrate a ML approach to segment areas of interest from the SGU point-based photo labels. Maybe using Taglab or CVAT?

Relevant literature:
General segmentation:
https://arxiv.org/pdf/2003.06148.pdf
https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-segment-anything-with-sam.ipynb

Segment Anything Meets Point Tracking:
Repo on a joint model of point tracking and Segmentation

Ocean-related:
On Improving the Training of Models for the Semantic Segmentation of Benthic Communities from Orthographic Imagery
Automatic Semantic Segmentation of Benthic Habitats Using Images from Towed Underwater Camera in a Complex Shallow Water Environment

@jannesgg @biomobst from our last conversation with SGU it's clear to me we should have different ML model approaches and human-in-the-loop annotation labelling (whether CVAT, Taglab,...)

[victor-wildlife]

@jannesgg and @Diewertje11 here is the post on the use of "segment anything"

[jannesgg]

• Review Benjamin's branch for potential improvements to segmentation workflow
• Make use of the segmentation models from Ultralytics package