Question about Within-Class Online Clustering

[clgx00]

Hi, I'm interested in your work. After reading the paper, I'm confused that the goal of Within-Class Online Clustering is to map the pixels Ic to the K prototypes of class c. But how to know if pixels Ic belongs to class c? Did you use Ground Truth in this step? So how do you set it up when testing?

Hope to receive your reply, thanks!

[wenguanwang]

@clgx00 Within-class clustering is unsupervised -- for each class c, we need to automatically find K prototypes. However, the whole task setting is supervised -- for each pixel, we know the corresponding class.

This is a compelling feature of our algorithm -- integrating unsupervised, within-class subpattern mining into supervised, per-pixel classification.

[clgx00]

@wenguanwang Yes, this is an innovative point of the algorithm. Does this mean that online clustering was not used when testing?

[wenguanwang]

@clgx00
Of course not used. The clustering is to find class prototypes as references for classification. Once trained, the class prototypes are stored and used for classification.

[clgx00]

Thank you for your response! It is really a great work.

[yunpengt]

i want to know which part is online clustering? i can't find it..

[tfzhou]

@yunpengt please check here:

ProtoSeg/lib/models/nets/hrnet.py

Line 54 in 1c4a778

class HRNet_W48_Proto(nn.Module):

[NJNUCS]

Hello, thank you for your excellent work, I would like to ask what are the two values returned in the code by sink_horn, what do q and indexes mean respectively?

[QChhh123]

Hello, thank you for your excellent work, I would like to ask what are the two values returned in the code by sink_horn, what do q and indexes mean respectively?

I have the same question, do you have any idea now? Thank you very much.