Pyramid Fusion Transformer for Semantic Segmentation

The recently proposed MaskFormer [Cheng et al. (2021)] gives a refreshed perspective on the task of semantic segmentation: it shifts from the popular pixel-level classification paradigm to a mask-level classification method. In essence, it generates paired probabilities and masks corresponding to category segments and combines them during inference for the segmentation maps. In our study, we find that the per-mask classification decoder on top of a single-scale feature is not effective enough to extract reliable probabilities or masks. To mine for rich semantic information across the feature pyramid, we propose a transformer-based Pyramid Fusion Transformer (PFT) for per-mask approach semantic segmentation with multi-scale features. The proposed transformer decoder performs cross-attention between the learnable queries and each spatial feature from the feature pyramid in parallel and uses cross-scale inter-query attention to exchange complimentary information. We achieve competitive performance on three widely used semantic segmentation datasets. In particular, on ADE20K validation set, our result with Swin-B backbone surpasses that of MaskFormer's with a much larger Swin-L backbone in both single-scale and multi-scale inference, achieving 54.1 mIoU and 55.7 mIoU respectively. Using a Swin-L backbone, we achieve single-scale 56.1 mIoU and multi-scale 57.4 mIoU, obtaining state-of-the-art performance on the dataset. Extensive experiments on three widely used semantic segmentation datasets verify the effectiveness of our proposed method.