the_architect_ai
the_architect_ai t1_j71izep wrote
I suggest you just dive straight in. Part of learning is to find out what you don’t know and slowly cover your bases from there.
the_architect_ai t1_j6weyc4 wrote
Has already been done. plurabis
the_architect_ai t1_j5qks8o wrote
Reply to [D] CVPR Reviews are out by banmeyoucoward
Phd student here.
1 accept (5), 2 Borderline (3). What are the chances of my paper getting accepted?
the_architect_ai t1_j9h7but wrote
Reply to [Discussion] ML on extremely large datasets and images by deluded_soul
Use binning/ quantisation to reduce image size. Look into voxelisation.
Transformers can capture long range spatial interactions but computation is hefty. Might have to downsize first.
In ViT, tokenization is applied on patches. You might need a 3D CNN to extract voxel tokens.
There are many ways to reduce computational costs via attention-ing. In the paper Perceiver I/O by deepmind, a bottleneck cross attention layer is applied.