arxiv: https://arxiv.org/abs/1803.01534
this paper propose a network structure(“PANet”) with bottom up path augmentation and a few other tweak methods which results in better instance segmentation performance.
(more…)arxiv: https://arxiv.org/pdf/1807.06521.pdf
trying to attach attention module to CNN. but instead of blindly attaching it which would compute a 3D attention map which is computationally expensive, this work proposes to compute spatial attention and channel attention separately which achieves a similar effect with much less parameters.
(more…)arxiv: https://arxiv.org/abs/1406.4729
pools a fixed number of features from final feature map of backbone which can be fed to the dense layer afterwards, thus allowing network to work in a non-fixed input size manner.
(more…)