  Existing methods of multiple human parsing (MHP) apply statistical models to
acquire underlying associations between images and labeled body parts. However,
acquired associations often contain many spurious correlations that degrade
model generalization, leading statistical models to be vulnerable to visually
contextual variations in images (e.g., unseen image styles/external
interventions). To tackle this, we present a causality inspired parsing
paradigm termed CIParsing, which follows fundamental causal principles
involving two causal properties for human parsing (i.e., the causal diversity
and the causal invariance). Specifically, we assume that an input image is
constructed by a mix of causal factors (the characteristics of body parts) and
non-causal factors (external contexts), where only the former ones cause the
generation process of human parsing.Since causal/non-causal factors are
unobservable, a human parser in proposed CIParsing is required to construct
latent representations of causal factors and learns to enforce representations
to satisfy the causal properties. In this way, the human parser is able to rely
on causal factors w.r.t relevant evidence rather than non-causal factors w.r.t
spurious correlations, thus alleviating model degradation and yielding improved
parsing ability. Notably, the CIParsing is designed in a plug-and-play fashion
and can be integrated into any existing MHP models. Extensive experiments
conducted on two widely used benchmarks demonstrate the effectiveness and
generalizability of our method.
