  Part-level attribute parsing is a fundamental but challenging task, which
requires the region-level visual understanding to provide explainable details
of body parts. Most existing approaches address this problem by adding a
regional convolutional neural network (RCNN) with an attribute prediction head
to a two-stage detector, in which attributes of body parts are identified from
local-wise part boxes. However, local-wise part boxes with limit visual clues
(i.e., part appearance only) lead to unsatisfying parsing results, since
attributes of body parts are highly dependent on comprehensive relations among
them. In this article, we propose a Knowledge Embedded RCNN (KE-RCNN) to
identify attributes by leveraging rich knowledges, including implicit knowledge
(e.g., the attribute ``above-the-hip'' for a shirt requires visual/geometry
relations of shirt-hip) and explicit knowledge (e.g., the part of ``shorts''
cannot have the attribute of ``hoodie'' or ``lining''). Specifically, the
KE-RCNN consists of two novel components, i.e., Implicit Knowledge based
Encoder (IK-En) and Explicit Knowledge based Decoder (EK-De). The former is
designed to enhance part-level representation by encoding part-part relational
contexts into part boxes, and the latter one is proposed to decode attributes
with a guidance of prior knowledge about \textit{part-attribute} relations. In
this way, the KE-RCNN is plug-and-play, which can be integrated into any
two-stage detectors, e.g., Attribute-RCNN, Cascade-RCNN, HRNet based RCNN and
SwinTransformer based RCNN. Extensive experiments conducted on two challenging
benchmarks, e.g., Fashionpedia and Kinetics-TPS, demonstrate the effectiveness
and generalizability of the KE-RCNN. In particular, it achieves higher
improvements over all existing methods, reaching around 3% of AP on
Fashionpedia and around 4% of Acc on Kinetics-TPS.
