  The segmentation task has traditionally been formulated as a complete-label
pixel classification task to predict a class for each pixel from a fixed number
of predefined semantic categories shared by all images or videos. Yet,
following this formulation, standard architectures will inevitably encounter
various challenges under more realistic settings where the scope of categories
scales up (e.g., beyond the level of 1k). On the other hand, in a typical image
or video, only a few categories, i.e., a small subset of the complete label are
present. Motivated by this intuition, in this paper, we propose to decompose
segmentation into two sub-problems: (i) image-level or video-level multi-label
classification and (ii) pixel-level rank-adaptive selected-label
classification. Given an input image or video, our framework first conducts
multi-label classification over the complete label, then sorts the complete
label and selects a small subset according to their class confidence scores. We
then use a rank-adaptive pixel classifier to perform the pixel-wise
classification over only the selected labels, which uses a set of rank-oriented
learnable temperature parameters to adjust the pixel classifications scores.
Our approach is conceptually general and can be used to improve various
existing segmentation frameworks by simply using a lightweight multi-label
classification head and rank-adaptive pixel classifier. We demonstrate the
effectiveness of our framework with competitive experimental results across
four tasks, including image semantic segmentation, image panoptic segmentation,
video instance segmentation, and video semantic segmentation. Especially, with
our RankSeg, Mask2Former gains +0.8%/+0.7%/+0.7% on ADE20K panoptic
segmentation/YouTubeVIS 2019 video instance segmentation/VSPW video semantic
segmentation benchmarks respectively.
