Thank you for your attracting work "PERSONALIZE SEGMENT ANYTHING MODEL WITH ONE SHOT". There are some problems getting ready to be answered.
Firstly, how to process "Token-to-Image Cross-Attention 和 Image-to-Token Cross-Attention" in PerSAM's Decoder? I cannot find the relevant code in the open code, though I find
"sim = (sim - sim.mean()) / torch.std(sim)
sim = F.interpolate(sim.unsqueeze(0).unsqueeze(0), size=(64, 64), mode="bilinear")
attn_sim = sim.sigmoid_().unsqueeze(0).flatten(3)
".
Secondly, how to achieve the formula(8) ? I cannot find the implementation in the open code.
Thank you for your attracting work "PERSONALIZE SEGMENT ANYTHING MODEL WITH ONE SHOT". There are some problems getting ready to be answered.
Firstly, how to process "Token-to-Image Cross-Attention 和 Image-to-Token Cross-Attention" in PerSAM's Decoder? I cannot find the relevant code in the open code, though I find
"sim = (sim - sim.mean()) / torch.std(sim)
sim = F.interpolate(sim.unsqueeze(0).unsqueeze(0), size=(64, 64), mode="bilinear")
attn_sim = sim.sigmoid_().unsqueeze(0).flatten(3)
".
Secondly, how to achieve the formula(8) ? I cannot find the implementation in the open code.