  Current shadow detection methods perform poorly when detecting shadow regions
that are small, unclear or have blurry edges. In this work, we attempt to
address this problem on two fronts. First, we propose a Fine Context-aware
Shadow Detection Network (FCSD-Net), where we constraint the receptive field
size and focus on low-level features to learn fine context features better.
Second, we propose a new learning strategy, called Restore to Detect (R2D),
where we show that when a deep neural network is trained for restoration
(shadow removal), it learns meaningful features to delineate the shadow masks
as well. To make use of this complementary nature of shadow detection and
removal tasks, we train an auxiliary network for shadow removal and propose a
complementary feature learning block (CFL) to learn and fuse meaningful
features from shadow removal network to the shadow detection network. We train
the proposed network, FCSD-Net, using the R2D learning strategy across multiple
datasets. Experimental results on three public shadow detection datasets (ISTD,
SBU and UCF) show that our method improves the shadow detection performance
while being able to detect fine context better compared to the other recent
methods.
