BoxeR: Box-Attention for 2D and 3D Transformers
By Duy-Kien Nguyen, Jihong Ju, Olaf Booij, Martin R. Oswald, Cees Snoek. This repository is an official implementation of the paper BoxeR: Box-Attention for 2D and 3D Transformers. Introduction TL; DR. BoxeR is a Transformer-based network for end-to-end 2D object detection and instance segmentation, along with 3D object detection. The core of the network is Box-Attention which predicts regions of interest to attend by learning the transformation (translation, scaling, and rotation) from reference windows, yielding competitive performance on several vision […]
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