You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: More formally, as illustrated in fig. 3(Top), the deep spatial features of the noisy image \phi(z_{t}) are projected to a query matrix Q=\ell_{Q}(\phi(z_{t})), and the textual embedding is projected to a key matrix K=\ell_{K}(\psi(\mathcal{P})) and a value matrix V=\ell_{V}(\psi(\mathcal{P})), via learned linear projections \ell_{Q},\ell_{K},\ell_{V}.The attention maps are thenM=\text{Softmax}\left(\frac{QK^{T}}{\sqrt{d}}\right),(1)where the cell M_{ij} defines the weight of the value of the j-th token on the pixel i, and where d is the latent projection dimension of the keys and queries. Finally, the cross-attention output is defined to be \widehat{\phi}\left(z_{t}\right)=MV, which is then used to update the spatial features \phi(z_{t}). Intuitively, the cross-attention output MV is a weighted average of the values V where the weights are the attention maps M, which are correlated to the similarity between Q and K.In practice, to increase their expressiveness, multi-head attention NIPS2017_3f5ee243  is used in parallel, and then the results are concatenated and passed through a learned linear layer to get the final output.

Question: They generated the cross-attention output weight by calculating the similarity between spatial features of the noise image and textual embedding. Is it right?
True, It's correlated to the similarity between a query matrix of projected noisy image "Q" and a key matrix of a projected textual embedding "K".