# FADE: Fusing the Assets of Decoder and Encoder for Task-Agnostic Upsampling

arXiv: https://arxiv.org/abs/2207.10392 · HTML: https://mlsanity.com/paper/2207.10392/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 42 references that are in the map.
- First posted on arXiv 21 Jul 2022.

## Builds on (most cited first, 30 of 33)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [Unified Perceptual Parsing for Scene Understanding](https://mlsanity.com/paper/1807.10221/) (2018)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation](https://mlsanity.com/paper/1611.06612/) (2016)
- [Boundary IoU: Improving Object-Centric Image Segmentation Evaluation](https://mlsanity.com/paper/2103.16562/) (2021)
- [Deep Image Matting](https://mlsanity.com/paper/1703.03872/) (2017)
- [CARAFE: Content-Aware ReAssembly of FEatures](https://mlsanity.com/paper/1905.02188/) (2019)
- [Indices Matter: Learning to Index for Deep Image Matting](https://mlsanity.com/paper/1908.00672/) (2019)
- [In-Place Activated BatchNorm for Memory-Optimized Training of DNNs](https://mlsanity.com/paper/1712.02616/) (2017)
- [Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation](https://mlsanity.com/paper/2104.05239/) (2021)
- [Learning Affinity-Aware Upsampling for Deep Image Matting](https://mlsanity.com/paper/2011.14288/) (2020)
- [Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation](https://mlsanity.com/paper/1903.02120/) (2019)

## Built on by (most cited first, 25 of 25)

- [FeatUp: A Model-Agnostic Framework for Features at Any Resolution](https://mlsanity.com/paper/2403.10516/) (2024)
- [Learning to Upsample by Learning to Sample](https://mlsanity.com/paper/2308.15085/) (2023)
- [Frequency-aware Feature Fusion for Dense Image Prediction](https://mlsanity.com/paper/2408.12879/) (2024)
- [SAPA: Similarity-Aware Point Affiliation for Feature Upsampling](https://mlsanity.com/paper/2209.12866/) (2022)
- [LiFT: A Surprisingly Simple Lightweight Feature Transform for Dense ViT Descriptors](https://mlsanity.com/paper/2403.14625/) (2024)
- [LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models](https://mlsanity.com/paper/2504.14032/) (2025)
- [JAFAR: Jack up Any Feature at Any Resolution](https://mlsanity.com/paper/2506.11136/) (2025)
- [A Refreshed Similarity-based Upsampler for Direct High-Ratio Feature Upsampling](https://mlsanity.com/paper/2407.02283/) (2024)
- [FADE: A Task-Agnostic Upsampling Operator for Encoder-Decoder Architectures](https://mlsanity.com/paper/2407.13500/) (2024)
- [FreqU-FNet: Frequency-Aware U-Net for Imbalanced Medical Image Segmentation](https://mlsanity.com/paper/2505.17544/) (2025)
- [On Point Affiliation in Feature Upsampling](https://mlsanity.com/paper/2307.08198/) (2023)
- [Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation](https://mlsanity.com/paper/2505.02075/) (2025)
- [Dynamic Snake Upsampling Operater and Boundary-Skeleton Weighted Loss for Tubular Structure Segmentation](https://mlsanity.com/paper/2505.08525/) (2025)
- [First Place Solution to the MLCAS 2025 GWFSS Challenge: The Devil is in the Detail and Minority](https://mlsanity.com/paper/2508.17305/) (2025)
- [Lighten CARAFE: Dynamic Lightweight Upsampling with Guided Reassemble Kernels](https://mlsanity.com/paper/2410.22139/) (2024)
- [LDA-AQU: Adaptive Query-guided Upsampling via Local Deformable Attention](https://mlsanity.com/paper/2411.19585/) (2024)
- [Fourier-Guided Attention Upsampling for Image Super-Resolution](https://mlsanity.com/paper/2508.10616/) (2025)
- [DyGLNet: Hybrid Global-Local Feature Fusion with Dynamic Upsampling for Medical Image Segmentation](https://mlsanity.com/paper/2509.12763/) (2025)
- [SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders](https://mlsanity.com/paper/2511.11015/) (2025)
- [Tracking spatial temporal details in ultrasound long video via wavelet analysis and memory bank](https://mlsanity.com/paper/2512.15066/) (2025)
- [StegaFFD: Privacy-Preserving Face Forgery Detection via Fine-Grained Steganographic Domain Lifting](https://mlsanity.com/paper/2603.02886/) (2026)
- [A3-FPN: Asymptotic Content-Aware Pyramid Attention Network for Dense Visual Prediction](https://mlsanity.com/paper/2604.10210/) (2026)
- [RaysUp: Ultra-light Universal Feature Upsampling via Geometry-Aware Ray Representation](https://mlsanity.com/paper/2606.22749/) (2026)
- [PixelUp: Zero-Shot Semantic Feature Upsampling for Fine-Grained Vision Tasks](https://mlsanity.com/paper/2608.02792/) (2026)
- [EmbPASS: Towards Cross-Embodiment Open Panoramic Segmentation](https://mlsanity.com/paper/2610.03248/) (2026)

## Abstract (arXiv)

We consider the problem of task-agnostic feature upsampling in dense prediction where an upsampling operator is required to facilitate both region-sensitive tasks like semantic segmentation and detail-sensitive tasks such as image matting. Existing upsampling operators often can work well in either type of the tasks, but not both. In this work, we present FADE, a novel, plug-and-play, and task-agnostic upsampling operator. FADE benefits from three design choices: i) considering encoder and decoder features jointly in upsampling kernel generation; ii) an efficient semi-shift convolutional operator that enables granular control over how each feature point contributes to upsampling kernels; iii) a decoder-dependent gating mechanism for enhanced detail delineation. We first study the upsampling properties of FADE on toy data and then evaluate it on large-scale semantic segmentation and image matting. In particular, FADE reveals its effectiveness and task-agnostic characteristic by consistently outperforming recent dynamic upsampling operators in different tasks. It also generalizes well across convolutional and transformer architectures with little computational overhead. Our work additionally provides thoughtful insights on what makes for task-agnostic upsampling. Code is available at: http://lnkiy.in/fade_in