You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
the assumption P(bg | O_k = 0) = 1 can be easily understand, but i wonder what's your understanding of $P(bg | O_k = 1)P(O_k = 1)$ in the rvalue of the formula shown above. here's my opinions:
you use the law of full probability to decompose the entire process into 1st proposal stage and 2nd refine & classfication stage. P(O_k = 1) means the probability of foreground proposal prediction, while P(bg | O_k = 1) means given a foreground proposal but actually it belongs to background, the 2nd stage revise it to the background.
is that understanding correct?
/*
following that, here's another question: how to model the P(bg | O_k = 1) in you network and how to prove you do not acutally model the P(bg | O_k = 0) instead? is that the assumption P(bg | O_k = 0) = 1 works?
*/
i noticed something wrong with the question annotated above: the last linear layer (i.e. logistic/softmax regression) actually model the P(C_k | O_k) rather than specific condition O_k = 0 or O_k = 1, so the last question may not exist. backup for more people with the same misunderstanding.
appreciate your reply, thanks!
Links to the relevant documentation/comment:
The text was updated successfully, but these errors were encountered:
📚 Documentation
i noticed the formula between (2) and (3) in your original paper:
log(P_bg) = log(P(bg | O_k = 1)P(O_k = 1) + P(O_k = 0))
the assumption P(bg | O_k = 0) = 1 can be easily understand, but i wonder what's your understanding of$P(bg | O_k = 1)P(O_k = 1)$ in the rvalue of the formula shown above. here's my opinions:
you use the law of full probability to decompose the entire process into 1st proposal stage and 2nd refine & classfication stage. P(O_k = 1) means the probability of foreground proposal prediction, while P(bg | O_k = 1) means given a foreground proposal but actually it belongs to background, the 2nd stage revise it to the background.
is that understanding correct?
/*
following that, here's another question: how to model the P(bg | O_k = 1) in you network and how to prove you do not acutally model the P(bg | O_k = 0) instead? is that the assumption P(bg | O_k = 0) = 1 works?
*/
i noticed something wrong with the question annotated above: the last linear layer (i.e. logistic/softmax regression) actually model the P(C_k | O_k) rather than specific condition O_k = 0 or O_k = 1, so the last question may not exist. backup for more people with the same misunderstanding.
appreciate your reply, thanks!
The text was updated successfully, but these errors were encountered: