  Unsupervised object discovery (UOD) has recently shown encouraging progress
with the adoption of pre-trained Transformer features. However, current methods
based on Transformers mainly focus on designing the localization head (e.g.,
seed selection-expansion and normalized cut) and overlook the importance of
improving Transformer features. In this work, we handle UOD task from the
perspective of feature enhancement and propose FOReground guidance and
MUlti-LAyer feature fusion for unsupervised object discovery, dubbed FORMULA.
Firstly, we present a foreground guidance strategy with an off-the-shelf UOD
detector to highlight the foreground regions on the feature maps and then
refine object locations in an iterative fashion. Moreover, to solve the scale
variation issues in object detection, we design a multi-layer feature fusion
module that aggregates features responding to objects at different scales. The
experiments on VOC07, VOC12, and COCO 20k show that the proposed FORMULA
achieves new state-of-the-art results on unsupervised object discovery. The
code will be released at https://github.com/VDIGPKU/FORMULA.
