I am trying to understand the Siamese networks . In this vector is calculated for an object (say an image) and a distance metric is applied (say manhatten) on two vectors produced by the neural network(s). The idea was applied mostly to images in the tutorials provided on internet.
If I compare it with Gensim semantic similarity, there also we have vectors of two objects (words or sentences) and then do a cosine similarity to calculate the difference. (remember example of King-man+woman=Queen).
Am I missing some aspects of Siamese networks or these are actually same?