Thank you for your reply!
Sorry for the confusion, but I mean inserting an image sequence in randomized order (e.g. [9,0,3,6,5,2,1,8,7,4], there is a sequence of 10 images here), into the UNet, and we expect the network to learn and re-group the image sequence into [0,1,2,3,4,5,6,7,8,9].
Now that I have read much more articles, I realized that this is termed as permutation invariance.
Some suggest adding a regularization term that penalizes the incorrect order.
(https://papers.nips.cc/paper/2020/file/d58f36f7679f85784d8b010ff248f898-Paper.pdf)
While some suggest that we can “add permutation invariance” feature in the hidden layers.
(machine learning - How could we build a neural network that is invariant to permutations of the inputs? - Artificial Intelligence Stack Exchange)