  Recently, there is growing attention on one-stage panoptic segmentation
methods which aim to segment instances and stuff jointly within a fully
convolutional pipeline efficiently. However, most of the existing works
directly feed the backbone features to various segmentation heads ignoring the
demands for semantic and instance segmentation are different: The former needs
semantic-level discriminative features, while the latter requires features to
be distinguishable across instances. To alleviate this, we propose to first
predict semantic-level and instance-level correlations among different
locations that are utilized to enhance the backbone features, and then feed the
improved discriminative features into the corresponding segmentation heads,
respectively. Specifically, we organize the correlations between a given
location and all locations as a continuous sequence and predict it as a whole.
Considering that such a sequence can be extremely complicated, we adopt
Discrete Fourier Transform (DFT), a tool that can approximate an arbitrary
sequence parameterized by amplitudes and phrases. For different tasks, we
generate these parameters from the backbone features in a fully convolutional
way which is optimized implicitly by corresponding tasks. As a result, these
accurate and consistent correlations contribute to producing plausible
discriminative features which meet the requirements of the complicated panoptic
segmentation task. To verify the effectiveness of our methods, we conduct
experiments on several challenging panoptic segmentation datasets and achieve
state-of-the-art performance on MS COCO with $45.1$\% PQ and ADE20k with
$32.6$\% PQ.
