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Recently, I have been studying this paper and I am not very clear about formula 4 in the paper. The spatial weight map M is generated for each type of manipulation operation. Therefore, if there are several annotations in the manipulation sequence, there should be several M. So, in formula 4, M should be the sum of all M for each operation of each input? I am not very clear about this calculation.
The text was updated successfully, but these errors were encountered:
yunfeiheng
changed the title
spatial enhence cross_attention
Spatially Enhanced Cross-Attention
Mar 29, 2023
Recently, I have been studying this paper and I am not very clear about formula 4 in the paper. The spatial weight map M is generated for each type of manipulation operation. Therefore, if there are several annotations in the manipulation sequence, there should be several M. So, in formula 4, M should be the sum of all M for each operation of each input? I am not very clear about this calculation.
i have a configuration quesion but i cannot reslove it,would someone can help me?
Traceback (most recent call last):
File "D:/seqdeepfake/SeqDeepFake/train.py", line 435, in
cfg = Config(args.cfg)
File "D:\seqdeepfake\SeqDeepFake\models\configuration.py", line 6, in init
with open(config_file) as f:
TypeError: expected str, bytes or os.PathLike object, not NoneType
Recently, I have been studying this paper and I am not very clear about formula 4 in the paper. The spatial weight map M is generated for each type of manipulation operation. Therefore, if there are several annotations in the manipulation sequence, there should be several M. So, in formula 4, M should be the sum of all M for each operation of each input? I am not very clear about this calculation.
The text was updated successfully, but these errors were encountered: