Peripheral Vision Transformers - Supplementary Materials - Juhong Min

B, we present additional details on peripheral region classification presented in Sec 4.1. We conclude this paper with a short discussion on potential impacts of our work in Sec. Recall the definition of the peripheral position encoding introduced in Sec. The parameterization in Eq. 3 is applied for all the layers ( Our first step is to prove the parameterization (Eq. 3) provides local attention after the Note that the negation in Eq. 8 gives the inequality of PP (R; W Step 2. We now show that the respective size and strength of local attention in the peripheral position Figure S2: Peripheral vision of human eye (left). We found that Eq. 23 gives radius of 1.5 given para-central angle of 8, resulting in quite narrow interval: Assume the position-based function at each head is learned to perform'hard attention' on one of its surrounding positions, i.e., an extreme semi-dynamic attention .