A Refreshed Similarity-Based Upsampler for Direct High-Ratio Feature Upsampling.

Abstract

Feature upsampling is a fundamental and indispensable ingredient of almost all current network structures for dense prediction tasks. Very recently, a popular similarity-based feature upsampling pipeline has been proposed, which utilizes a high-resolution (HR) feature as guidance to help upsample the low-resolution (LR) deep feature based on their local similarity. Albeit achieving promising performance, this pipeline has specific limitations in methodological designs: 1) HR query and LR key features are not well aligned in a controllable manner; 2) the similarity between query-key features is computed based on the fixed inner product form, lacking flexibility; and 3) neighbor selection is coarsely operated on LR features, resulting in mosaic artifacts. These shortcomings make the existing methods along this pipeline primarily applicable to hierarchical network architectures with iterative features as guidance, and they are not readily extended to a broader range of structures, especially for a direct high-ratio upsampling. Against these issues, we thoroughly refresh this pipeline and meticulously optimize every methodological design. Specifically, we first propose an explicitly controllable query-key feature alignment from both semantic-aware and detail-aware perspectives and then construct a parameterized paired central difference convolution block for flexibly calculating the similarity between the well-aligned query-key features. Besides, we develop a fine-grained neighbor selection strategy on HR features, which is simple yet effective for alleviating mosaic artifacts. Based on these careful designs, we systematically construct a refreshed similarity-based feature upsampling framework named ReSFU. Based on 13 types of network backbones, comprehensive experiments substantiate that only in a simple and direct high-ratio upsampling manner, our ReSFU consistently achieves satisfactory performance on six tasks, including semantic segmentation, medical image segmentation, instance segmentation, panoptic segmentation, object detection, and monocular depth estimation, showing superior generality and ease of deployment beyond the existing upsamplers. Codes are available at https://github.com/zmhhmz/ReSFU.