SSPNet: Scale and Spatial Priors Guided Generalizable and Interpretable
  Pedestrian Attribute Recognition
Jifeng Shen Teng Guo Xin Zuo Heng Fan Wankou Yang
Global feature based Pedestrian Attribute Recognition (PAR) models are often
poorly localized when using Grad-CAM for attribute response analysis, which has
a significant impact on the interpretability, generalizability and performance.
Previous researches have attempted to improve generalization and interpretation
through meticulous model design, yet they often have neglected or underutilized
effective prior information crucial for PAR. To this end, a novel Scale and
Spatial Priors Guided Network (SSPNet) is proposed for PAR, which is mainly
composed of the Adaptive Feature Scale Selection (AFSS) and Prior Location
Extraction (PLE) modules. The AFSS module learns to provide reasonable scale
prior information for different attribute groups, allowing the model to focus
on different levels of feature maps with varying semantic granularity. The PLE
module reveals potential attribute spatial prior information, which avoids
unnecessary attention on irrelevant areas and lowers the risk of model
over-fitting. More specifically, the scale prior in AFSS is adaptively learned
from different layers of feature pyramid with maximum accuracy, while the
spatial priors in PLE can be revealed from part feature with different
granularity (such as image blocks, human pose keypoint and sparse sampling
points). Besides, a novel IoU based attribute localization metric is proposed
for Weakly-supervised Pedestrian Attribute Localization (WPAL) based on the
improved Grad-CAM for attribute response mask. The experimental results on the
intra-dataset and cross-dataset evaluations demonstrate the effectiveness of
our proposed method in terms of mean accuracy (mA). Furthermore, it also
achieves superior performance on the PCS dataset for attribute localization in
terms of IoU. Code will be released at https://github.com/guotengg/SSPNet.