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A Refreshed Similarity-Based Upsampler for Direct High-Ratio Feature Upsampling
IEEE Transactions on Neural Networks and Learning Systems
|December 4, 2025
Summary
Researchers refreshed a similarity-based feature upsampling pipeline, improving feature alignment and similarity calculation. The new ReSFU framework enhances performance across diverse dense prediction tasks with high-ratio upsampling.
Area of Science:
- Computer Vision
- Deep Learning
- Image Processing
Background:
- Feature upsampling is crucial for dense prediction tasks in deep learning.
- Existing similarity-based upsampling methods have limitations in feature alignment, similarity computation flexibility, and neighbor selection, restricting their applicability.
- These limitations hinder direct high-ratio upsampling and broad integration into various network architectures.
Purpose of the Study:
- To address the limitations of current similarity-based feature upsampling pipelines.
- To develop a refreshed and more flexible feature upsampling framework.
- To enable effective direct high-ratio upsampling applicable to a wider range of network structures.
Main Methods:
- Proposed an explicitly controllable query-key feature alignment mechanism (semantic-aware and detail-aware).
- Introduced a parameterized paired central difference convolution for flexible similarity calculation.
- Developed a fine-grained neighbor selection strategy on high-resolution features to mitigate artifacts.
Main Results:
- Constructed a refreshed similarity-based feature upsampling framework named ReSFU.
- Demonstrated consistent satisfactory performance of ReSFU across 13 network backbones and six dense prediction tasks (semantic segmentation, medical image segmentation, instance segmentation, panoptic segmentation, object detection, monocular depth estimation).
- Showcased superior generality and ease of deployment compared to existing upsampling methods in direct high-ratio upsampling scenarios.
Conclusions:
- The proposed ReSFU framework effectively overcomes the limitations of previous similarity-based upsampling methods.
- ReSFU offers a versatile and robust solution for feature upsampling, enhancing performance across various computer vision tasks.
- The framework's design facilitates broader applicability and simpler integration into diverse deep learning architectures.
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