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arXiv · 2312.08268

Efficient Multi-Object Pose Estimation using Multi-Resolution Deformable Attention and Query Aggregation

Arul Selvam Periyasamy·Vladimir Tsaturyan·Sven Behnke
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Abstract

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.

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Arul Selvam Periyasamy, Vladimir Tsaturyan, Sven Behnke. 2023-12-13. Efficient Multi-Object Pose Estimation using Multi-Resolution Deformable Attention and Query Aggregation. https://arxiv.org/abs/2312.08268

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