Quasi-equilibrium Feature Pyramid Network for Salient Object Detection

Modern saliency detection models are based on the
encoder-decoder framework and they use different strategies to
fuse the multi-level features between the encoder and decoder
to boost representation power. Motivated by recent work in
implicit modelling, we propose to introduce an implicit function
to simulate the equilibrium state of the feature pyramid at infinite
depths. We question the existence of the ideal equilibrium and
thus propose a quasi-equilibrium model by taking the first-order
derivative into the black-box root solver using Taylor expansion.
It models more realistic convergence states and significantly
improves the network performance. We also propose a differentiable
edge extractor that directly extracts edges from the
saliency masks. By optimizing the extracted edges, the generated
saliency masks are naturally optimized on contour constraints
and the non-deterministic predictions are removed. We evaluate
the proposed methodology on five public datasets and extensive
experiments show that our method achieves new state-of-the-art
performances on six metrics across datasets.