Reproducing AffectNet results.

[sunggukcha]

Hello!

Thanks for sharing your works.

• How can I reproduce the affectnet results? The script seems to require some preprocess.

• Can you share affectnet pretrained model?

Regards, Sungguk Cha

[kdhht2334]

Hi,

First of all, I upload elim_category.py and update corresponding details. Please check.

Note You must add estimated age scores to your train/val.script. And you may choose two options. 1) Use pretrained age estimation model to complete your train/val.script (link: https://github.com/yu4u/age-estimation-pytorch) 2) Borrow both age_script/training_age.csv and age_script/validation_age.csv to complete your train/val.script

• Other metadata can be obtained from AffectNet official website :)

Unfortunately, I cannot share pretrained model of AffectNet..I cannot find the weights for now. But I can tell that you can easily get following bench-marking Table using elim_category.py!

Method	Acc.
Baseline [1]	0.58
VGG-Face [2]	0.60
HO-Conv [3]	0.59
ELIM-Age (R18)	0.611
Face2Exp [4]	0.64

References [1] A. Mollahosseini et al., Affectnet: A database for facial expression, valence, and arousal computing in the wild, TAC 2017. [2] D. Kollias et al., Generating faces for affect analysis, ArXiv 2018. [3] J. Kossaifi et al., Factorized higher-order cnns with an application to spatio-temporal emotion estimation, CVPR 2020. [4] D. Zeng et al., Face2Exp: combating data biases for facial expression recognition, CVPR 2022.

[sunggukcha]

Thanks for your reply. My interest was Valence-Arousal prediction in AffectNet dataset. In this case, can I get helped?

[kdhht2334]

AffectNet was mainly used for classification purposes. However, you can also use it for valence-arousal regression purposes as follows.

For convenience, I update the elim_age.py file. Please refer to this file for AffectNet.

Also i'm inspired following link:

• https://github.com/behzadhsni/BReG-NeXt

[sunggukcha]

Sincerely thanks for your kind reply.

It is rather far from the question. "why do you prefer Aff-wild2 or AFEW-VA dataset than AffectNet (valence-arousal)?" In my perspective of view, AffectNet has (1) more number of frames and (2) more number of identities, though it does not have temporal information. I want your opinion on the values of the Aff-Wild and AFEW-VA.

[kdhht2334]

1) AFEW-VA is suitable for fast check of model performance in exchange for a small number of frames. On the other hand, if you pre-process Aff-wild2's video clips, you can get about 1.4M frames. It is the most large-scale VA databse so far. Since these two datasets are from emotion recognition challenge (or variants), i think they are classified as wild DB.

• AffectNet is also a well-refined wild DB, but the domain gap between train and test datasets is large. I think AffectNet's performance is somewhat saturated. The reason is that the SOTA 7-class accuracy (%) of this AffectNet is less than 70.

2) Many IDs can be obtained from the AffectNet dataset, but the problem is that only 1 (or 2-3) samples are given for each ID. This fact becomes a major obstacle to the implementation of ELIM, which organizes datasets by domain. Therefore, I focused on AFEW-VA and Aff-wild2 given the raw data as video clips.

• cf) So, we had no choice but to experiment with AffectNet using the age group as the domain.

[sunggukcha]

You helped much. It was really nice for me to talk with you, @kdhht2334 .

Hope you happy. Regards, Sungguk Cha