  Object pose estimation is a long-standing problem in computer vision.
Recently, attention-based vision transformer models have achieved
state-of-the-art results in many computer vision applications. Exploiting the
permutation-invariant nature of the attention mechanism, a family of vision
transformer models formulate multi-object pose estimation as a set prediction
problem. However, existing vision transformer models for multi-object pose
estimation rely exclusively on the attention mechanism. Convolutional neural
networks, on the other hand, hard-wire various inductive biases into their
architecture. In this paper, we investigate incorporating inductive biases in
vision transformer models for multi-object pose estimation, which facilitates
learning long-range dependencies while circumventing the costly global
attention. In particular, we use multi-resolution deformable attention, where
the attention operation is performed only between a few deformed reference
points. Furthermore, we propose a query aggregation mechanism that enables
increasing the number of object queries without increasing the computational
complexity. We evaluate the proposed model on the challenging YCB-Video dataset
and report state-of-the-art results.
