The misalignment of features caused by pose and viewpoint variances is a crucial problem in Vehicle Re-Identification (ReID).
   Previous methods align the features by structuring the vehicles from pre-defined vehicle parts (such as logos, windows, etc.) or attributes, which are inefficient because of additional manual annotation.
   To align the features without requirements of additional annotation, this paper proposes a \textbf{Unstructured Feature Decoupling Network} (UFDN), which consists of a transformer-based feature decomposing head (TDH) and a novel cluster-based decoupling constraint (CDC).
   Different from the structured knowledge used in previous decoupling methods, we aim to achieve more flexible unstructured decoupled features with diverse discriminative information as shown in Fig. \ref{fig:intro}.
   The self-attention mechanism in the decomposing head helps the model preliminarily learn the discriminative decomposed features in a global scope. 
   To further learn diverse but aligned decoupled features, we introduce a cluster-based decoupling constraint consisting of a diversity constraint and an alignment constraint.
  Furthermore, we improve the alignment constraint into a modulated one to eliminate the negative impact of the outlier features that cannot align the clusters in semantics.
   Extensive experiments show the proposed UFDN achieves state-of-the-art performance on three popular Vehicle ReID benchmarks with both CNN and Transformer backbones.