Bidirectional Feature Pyramid Network with
Recurrent Attention Residual Modules
for Shadow Detection
Abstract. This paper presents a network to detect shadows by exploring and combining global context in deep layers and local context in
shallow layers of a deep convolutional neural network (CNN). There are
two technical contributions in our network design. First, we formulate the
recurrent attention residual (RAR) module to combine the contexts in
two adjacent CNN layers and learn an attention map to select a residual
and then refine the context features. Second, we develop a bidirectional
feature pyramid network (BFPN) to aggregate shadow contexts spanned
across different CNN layers by deploying two series of RAR modules in
the network to iteratively combine and refine context features: one series to refine context features from deep to shallow layers, and another
series from shallow to deep layers. Hence, we can better suppress false
detections and enhance shadow details at the same time. We evaluate
our network on two common shadow detection benchmark datasets: SBU and UCF. Experimental results show that our network outperforms
the best existing method with 34.88% reduction on SBU and 34.57%
reduction on UCF for the balance error rate