Comments (5)
For attention-based fusion keep --fusion True. When you do --fusion False, the concatenation fusion is enabled.
from multimodal-sentiment-analysis.
- please note that when you call --unimodal True, the generated pickle files override the ones provided in this repo. The ones you generated might not be optimised and giving you worse result. I strongly recommend use the ones which are included in this repo for e.g., unimodal_mosi_2way.pickle
from multimodal-sentiment-analysis.
@soujanyaporia I don't think it's because of --unimodal True, I can get the best result with Command 3, which is the same accuracy with Command 5.
from multimodal-sentiment-analysis.
What do you mean? I strongly recommend to use the pickle files provided in this repo. If you want to train the unimodals then please do optimise the networks to get the best result. Using the existing pickle files, anyone can get accuracy of 80%
from multimodal-sentiment-analysis.
@stdoo Actually, I also have the same question with you...
from multimodal-sentiment-analysis.
Related Issues (15)
- About Metric used: macro-fscore And attention visualization
- About MOSEI Dataset Classes
- looking for data processing part HOT 1
- How was the data created?
- Performance issues in run.py(P2)
- I want to know the Python version and the tensorflow version you used. HOT 1
- OpenSmile features for IEMOCAP
- not enough values to unpack (expected 13, got 7) HOT 7
- Error when running unimodal experiment HOT 1
- package version HOT 4
- code for dimension equalization HOT 3
- Would you please tell me which version of tensorflow you use? HOT 1
- unimodal_mosi_2way.pickle if the author provides changes, the accuracy will be reduced.
- Code for data processing HOT 7
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