- COCO dataloader for points sampling from object segmentation mask.
- COCO dataloader for points sampling from object boundary.
- Implement semantic segmentation using NFs
- Person segmentation (Simple binary segmentation of person vs background, N = 2)
- Apply random cropping, jittering
- Multi-class segmentation (N > 2)
- Implment boundary detection using NFs
- Learn
circles
distribution by adding extra NF and use it as a prior flow. - Sample from learned prior flow and feed it to subsequent NF.
- Improve backbones
- Use dilated convolutions
- Use leaky ReLU
- Use Flow++, SoftFlow, Gradient Boosted NF instead of CNFs.
- Vary the number of sample points
- Currently, 150 points
- 4 -> 15 -> 30 -> 60
- Implement hard negative mining scheme (or do we actually need it?)
Sampled from truncated normals (stddev=0.2)
Now able to cover multiple instances
At the early stage of training, the model focuses on covering a single person. Then, the Normalizing Flow is tring to learn harder cases where multimodality is present in a single image so that points could be sampled to cover multiple instances.
- Sae Young Kim