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SOLO: Segmenting Objects by Locations

April 2020

tl;dr: Single-shot instance segmentation.

Overall impression

The paper proposes a simple framework for instance segmentation directly. Essentially it is a YOLO architecture predicting additional HxW values at each cell. The HxW values are warped into a mask with the same resolution of the feature map. <-- However there is an important trick that reshapes the SxSx(HxW) into HxWx(SxS). MEInst showed that it is possible to directly predict the high dim vector per region, but for the entire image mask it is perhaps intractable.

Semantic segmentation classifies each pixel into a fixed number of categories. Instance segmentation has to deal with a varying number of instances. That is the biggest challenge. Instance segmentation can be sorted into top down approaches such as Mask RCNN and bottom up approaches such as Associate Embedding.

The decoupled SOLO idea is fabulous and I think is partially inspired by YOLACT by predicting prototype 2S masks.

This paper can be seen as an extension to the anchor-free object detection, such as FCOS and CenterNet, but with the important trick of reshaping the tensor. <-- See discussion in TensorMask.

Direct spatial2channel leads to spatial alignment too poor to guarantee good mask quality. (see natural representation in TensorMask). However it should be enough to guarantee the SxS order.

Key ideas

  • Grid cell: assumption is each cell of the SxS grid must belong to one individual instance. The instance mask branch has $H \times W \times S^2$ dimension.
  • FPN for multi-level prediction. Each feature map is only responsible for predicting masks within a certain scale range.
  • Dice loss to balance small and large masks. It leads to better performance than cross entropy or focal loss.
  • CoordConv to introduce spatial variance. (But why?)
  • Architecture
    • Center category: similar to CenterNet but on a coarser grid.
    • Mask category: for each positive grid, it corresponds to a channel in the HxW size feature map.
  • Decoupled SOLO: Predicting $S \times S$ mask is heavy. Instead the decoupled SOLO predicts $2 S$masks and use mask = element-wise multiplication of two masks. This yields the same results as vanilla SOLO.

Technical details

  • Feature alignment: The backbone has spatial size $H \times W$. There are two heads, one with $S \times S$ and the other with $H \times W$. Resampling/align is needed to map $H \times W \rightarrow S \times S$. Interpolation, adaptive pooling, RoIAlign all generate similar results.

Notes

  • What happens if we predict $S \times S$ masks for each grid? --> too coarse.
  • What happens if we predict $H \times W$ positions for each position? --> too much computation
  • So basically we need a coarse position prediction and a high resolution mask prediction. Thus SxS grid and HW resolution mask.
  • The formulation is essentially prediction a $S \times S \times (C + H \times W)$ feature map from a $H \times W \times Channels$ feature map, with FCN. The loss is done on warped masks and compare with GT.