Searching part-specific neural fabrics for human pose estimation

Highlights：

• We propose a novel micro and macro gradient-based architecture search space: parameterized cell-based neural fabric (CNF).

• With simple prior knowledge as guidance, our method automatically searches part-specific neural architectures to localize disentangled body parts, which extends the traditional part-based methods.

• Such part-specific neural architecture search can be seen as a variant of multi-task learning. It is a novel solution as multi-task neural architecture search for human pose estimation.

• The experiments show that the proposed model outperforms a hand-crafted part-based baseline model, and the resulting multiple part-specific architectures gain significant performance improvement against a single NAS-based architecture for the whole-body representation.