Foreground Object Search by Distilling Composite Image Feature
Bo Zhang Jiacheng Sui Li Niu
Foreground object search (FOS) aims to find compatible foreground objects for
a given background image, producing realistic composite image. We observe that
competitive retrieval performance could be achieved by using a discriminator to
predict the compatibility of composite image, but this approach has
unaffordable time cost. To this end, we propose a novel FOS method via
distilling composite feature (DiscoFOS). Specifically, the abovementioned
discriminator serves as teacher network. The student network employs two
encoders to extract foreground feature and background feature. Their
interaction output is enforced to match the composite image feature from the
teacher network. Additionally, previous works did not release their datasets,
so we contribute two datasets for FOS task: S-FOSD dataset with synthetic
composite images and R-FOSD dataset with real composite images. Extensive
experiments on our two datasets demonstrate the superiority of the proposed
method over previous approaches. The dataset and code are available at
https://github.com/bcmi/Foreground-Object-Search-Dataset-FOSD.