Learning-Deep-Learning

TensorMask: A Foundation for Dense Object Segmentation

April 2020

tl;dr: Aligned channel2spatial boosts the performance of instance segmentation than direct channel2spatial.

Overall impression

The paper proposes a relatively rigorous formulation for 4D tensor that unifies DeepMask and InstanceFCN into one framework. The paper seems to be overly complicated to convey two simple ideas: We need to align channel2spatial, and we need large masks for large objects.

The key question to dense instance segmentation: why cannot we naively adopt CenterNet architecture for instance segmentation?

The answer is that training a neural network with $480^2$ channels is intractable. Thus a tradeoff has to be made for $H \times W \times C$. Either predicts a coarse mask and rely on bilinear upsampling and feature alignment to gain better masks, as in TensorMask, or predicts full resolution masks at coarse location grids such as SOLO.

Key ideas

• Each mask is a HxW tensor. Dense prediction would require a 4D tensor representation, HxWxHxW. First two dimension are at each physical location. The latter two dimensions are the mask dimensions.
• Two main ideas:
  • First, channel2spatial can have Natural representation (direct channel2spatial) or Aligned representation (aligned channel2spatial). The authors demonstrated that aligned representation is one key ingredient to achieve better performance for dense mask prediction.
  • Second, being able to predict large masks for large object (tensor bipyramid) boosts instance segmentation performance. <– using constant resolution mask in MaskRCNN is one bottleneck for segmenting large objects.

Technical details

• Summary of technical details

Notes

• Questions and notes on how to improve/revise the current work