Rethinking Local and Global Feature Representation for Dense Prediction

Abstract

Although fully convolution networks (FCNs) have dominated dense prediction tasks (e.g., semantic segmentation, depth estimation and object detection) for decades, they are inherently limited in capturing long-range structured relationship with the layers of local kernels. While recent Transformer-based models have proven extremely successful in computer vision tasks by capturing global representation, they would deteriorate dense prediction results by over-smoothing the regions containing fine details (e.g., boundaries and small objects). To this end, we aim to provide an alternative perspective by rethinking local and global feature representation for the dense prediction task. Specifically, we deploy a Dual-Stream Convolution-Transformer architecture, called DSCT, by taking advantage of both the convolution and Transformer to learn a rich feature representation, combining with a task decoder to provide a powerful dense prediction model. DSCT extracts high resolution local feature representation from convolution layers and global feature representation from Transformer layers. With the local and global context modeled explicitly in every layer, the two streams can be combined with a decoder to perform task of semantic segmentation, monocular depth estimation or object detection. Extensive experiments show that DSCT can achieve superior performance on the three tasks above. For semantic segmentation, DSCT builds a new state of the art on Cityscapes validation set (83.31% mIoU) with only 80,000 training iterations and appealing performance (49.27% mIoU) on ADE20K validation set, outperforming most of the alternatives. For monocular depth estimation, our model achieves 2.423 RMSE on KITTI Eigen split, superior to most of the convolution or Transformer counterparts. For object detection, without using FPN, we can achieve 44.5% APb on COCO dataset when using Faster R-CNN, which is higher than Conformer.