Pervformer [new] -

Note: OOM = Out of Memory on 80GB A100.

A robot navigating a warehouse doesn't need to remember every pixel from 10 seconds ago. It needs to remember that a forklift moved a pallet (semantic) and that the path is now clear (spatial). PervFormer's memory probes act as a working memory, drastically reducing drift in SLAM-based systems. pervformer

import torch import torch.nn as nn class PervasiveAttention(nn.Module): def (self, dim, num_probes=64): super(). init () self.num_probes = num_probes # Learnable latent probes (global memory) self.probes = nn.Parameter(torch.randn(1, num_probes, dim)) Note: OOM = Out of Memory on 80GB A100

For years, the computer vision community has debated a fundamental trade-off: PervFormer's memory probes act as a working memory,

For automatic rotoscoping (cutting out a person from a video), previous models flickered when the person overlapped with a similar color background. PervFormer's pervasive attention keeps track of the person's identity across time, resulting in rock-solid masks. How to Implement (PyTorch Pseudo-Code) The core of PervFormer is surprisingly simple to integrate. Here is a minimal snippet showing the Pervasive Attention block:

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