no code implementations • 23 Apr 2023 • Shay Dekel, Yosi Keller, Aharon Bar-Hillel
We propose a novel formulation of deep networks that do not use dot-product neurons and rely on a hierarchy of voting tables instead, denoted as Convolutional Tables (CT), to enable accelerated CPU-based inference.
no code implementations • 5 Mar 2023 • Shay Dekel, Yosi Keller, Martin Cadik
In this work, we propose a cross-attention-based approach that utilizes CNN feature maps and a Transformer-Encoder, to compute the cross-attention between the activation maps of the image pairs, which is shown to be an improved equivalent of the 4D correlation volume, used in previous works.
