AAformer: Auto-Aligned Transformer for Person Re-Identification

Understand

In person re-identification (re-ID), extracting part-level features from person images has been verified to be crucial to offer fine-grained information.

• Most of the existing CNN-based methods only locate the human parts coarsely, or rely on pretrained human parsing models and fail in locating the identifiable nonhuman parts (e.g., knapsack).
• In this article, we introduce an alignment scheme in transformer architecture for the first time and propose the auto-aligned transformer (AAformer) to automatically locate both the human parts and nonhuman ones at patch level.
• We introduce the "Part tokens ([PART]s)", which are learnable vectors, to extract part features in the transformer.