Comments (8)
Basically, our FER model operates in the valence-arousal (VA) space.
So, the ground truth (GT) should be annotated for both valence and arousal respectively.
For example, an example file training.csv
is as follows.
filePath | valence | arousal |
---|---|---|
1/001/1.png | 0.2 | -0.1 |
... | ... | ... |
Here, the path of filePath
can be set arbitrarily by the user (e.g., 001/1.png
).
Fore more details, please refer this web site :)
https://ibug.doc.ic.ac.uk/resources/afew-va-database/
from elim_fer.
Sorry but which i want you explain to me is how do the image is annotated with 0.2 (valence) and 0.1 (arousal) ? What is it based on ? I have too many data with unlabeled, but I dont't know the way to annotate the value of valence and arousal
from elim_fer.
First of all, it is difficult to annotate valence and arousal values directly from a given image. Unlike common problems such as classification tasks, emotion annotation is not easy and involves some qualitative subjectivity.
There are roughly two options.
-
Self-annotation: Having the
subject of the image
annotate their feelings at that moment. The meaning of the valence and arousal axes must be taught in advance. -
Hire skilled annotators
The criteria for annotation in VA space can be found in the paper below:
- J. Posner et al. The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology, 2005.
from elim_fer.
Thanks for your answer, I will try it?
from elim_fer.
Sorry for the late reply :)
Sure!! But be careful below things:
- When annotating Valence-Arousal (VA) values, if the proportion of outlier samples is large, the model may not be trained properly.
- Here, an outlier sample is a case where the absolute value of the VA value is too large compared to other samples.
Fingers crossed!
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So what you mean is that I need to collect balanced datasets that fill all the VA space ?
from elim_fer.
Yes.
The best way is to collect the dataset with a distribution similar to Figure 4 in the paper below:
from elim_fer.
Thanks for your support, I got it.
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Related Issues (6)
- Reproducing AffectNet results. HOT 6
- Pre trained model HOT 1
- Estimation using my own facial image data. HOT 2
- License HOT 2
- offset of valence and arousal HOT 2
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