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.

使用多分辨率可变形注意力和查询聚合机制的视觉转换器模型在多目标姿态估计方面取得了最先进的结果。