WiCo: Win-win Cooperation of Bottom-up and Top-down Referring Image
  Segmentation
Zesen Cheng Peng Jin Hao Li Kehan Li Siheng Li Xiangyang Ji Chang Liu Jie Chen
The top-down and bottom-up methods are two mainstreams of referring
segmentation, while both methods have their own intrinsic weaknesses. Top-down
methods are chiefly disturbed by Polar Negative (PN) errors owing to the lack
of fine-grained cross-modal alignment. Bottom-up methods are mainly perturbed
by Inferior Positive (IP) errors due to the lack of prior object information.
Nevertheless, we discover that two types of methods are highly complementary
for restraining respective weaknesses but the direct average combination leads
to harmful interference. In this context, we build Win-win Cooperation (WiCo)
to exploit complementary nature of two types of methods on both interaction and
integration aspects for achieving a win-win improvement. For the interaction
aspect, Complementary Feature Interaction (CFI) provides fine-grained
information to top-down branch and introduces prior object information to
bottom-up branch for complementary feature enhancement. For the integration
aspect, Gaussian Scoring Integration (GSI) models the gaussian performance
distributions of two branches and weightedly integrates results by sampling
confident scores from the distributions. With our WiCo, several prominent
top-down and bottom-up combinations achieve remarkable improvements on three
common datasets with reasonable extra costs, which justifies effectiveness and
generality of our method.