FADE: A Task-Agnostic Upsampling Operator for Encoder–Decoder Architectures

The goal of this work is to develop a task-agnostic feature upsampling operator for dense prediction where the operator is required to facilitate not only region-sensitive tasks like semantic segmentation but also detail-sensitive tasks such as image matting. Prior upsampling operators often can wor...

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Vydané v:International journal of computer vision Ročník 133; číslo 1; s. 151 - 172
Hlavní autori: Lu, Hao, Liu, Wenze, Fu, Hongtao, Cao, Zhiguo
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.01.2025
Springer Nature B.V
Predmet:
Artificial Intelligence
Computer Imaging
Computer Science
Encoders-Decoders
Image Processing and Computer Vision
Image segmentation
Parameter sensitivity
Pattern Recognition
Pattern Recognition and Graphics
Semantic segmentation
Semantics
Vision
Instance segmentation
Feature upsampling
Semantic segmentation
Object detection
Image matting
Dense prediction
Depth estimation
ISSN:0920-5691, 1573-1405
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Shrnutí:The goal of this work is to develop a task-agnostic feature upsampling operator for dense prediction where the operator is required to facilitate not only region-sensitive tasks like semantic segmentation but also detail-sensitive tasks such as image matting. Prior upsampling operators often can work well in either type of the tasks, but not both. We argue that task-agnostic upsampling should dynamically trade off between semantic preservation and detail delineation, instead of having a bias between the two properties. In this paper, we present FADE, a novel, plug-and-play, lightweight, and task-agnostic upsampling operator by fusing the assets of decoder and encoder features at three levels: (i) considering both the encoder and decoder feature in upsampling kernel generation; (ii) controlling the per-point contribution of the encoder/decoder feature in upsampling kernels with an efficient semi-shift convolutional operator; and (iii) enabling the selective pass of encoder features with a decoder-dependent gating mechanism for compensating details. To improve the practicality of FADE, we additionally study parameter- and memory-efficient implementations of semi-shift convolution. We analyze the upsampling behavior of FADE on toy data and show through large-scale experiments that FADE is task-agnostic with consistent performance improvement on a number of dense prediction tasks with little extra cost. For the first time, we demonstrate robust feature upsampling on both region- and detail-sensitive tasks successfully. Code is made available at: https://github.com/poppinace/fade
Bibliografia:ObjectType-Article-1
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ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-024-02191-8