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Feature Upsampling

Deep features are a cornerstone of computer vision research, capturing image semantics and enabling the community to solve downstream tasks even in the zero- or few-shot regime. However, these features often lack the spatial resolution to directly perform dense prediction tasks like segmentation and depth prediction because models aggressively pool information over large areas. Feature Upsampling aims to recover this missing spatial resolution without impacting the space of the original deep features.

Papers

Showing 125 of 25 papers

TitleStatusHype
FeatUp: A Model-Agnostic Framework for Features at Any ResolutionCode5
LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation ModelsCode3
JAFAR: Jack up Any Feature at Any ResolutionCode3
ASCNet: Asymmetric Sampling Correction Network for Infrared Image DestripingCode2
SeaFormer++: Squeeze-enhanced Axial Transformer for Mobile Visual RecognitionCode2
Deep Image PriorCode1
Deep ViT Features as Dense Visual DescriptorsCode1
EfficientCD: A New Strategy For Change Detection Based With Bi-temporal Layers ExchangedCode1
FADE: A Task-Agnostic Upsampling Operator for Encoder-Decoder ArchitecturesCode1
FADE: Fusing the Assets of Decoder and Encoder for Task-Agnostic UpsamplingCode1
Joint Denoising and Demosaicking with Green Channel Prior for Real-world Burst ImagesCode1
Learning to Upsample by Learning to SampleCode1
Lighten CARAFE: Dynamic Lightweight Upsampling with Guided Reassemble KernelsCode1
Local and Global GANs with Semantic-Aware Upsampling for Image GenerationCode1
On Point Affiliation in Feature UpsamplingCode1
SAPA: Similarity-Aware Point Affiliation for Feature UpsamplingCode1
A Refreshed Similarity-based Upsampler for Direct High-Ratio Feature UpsamplingCode0
LDA-AQU: Adaptive Query-guided Upsampling via Local Deformable AttentionCode0
LiFT: A Surprisingly Simple Lightweight Feature Transform for Dense ViT DescriptorsCode0
Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive SegmentationCode0
CARAFE: Content-Aware ReAssembly of FEaturesCode0
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object DetectionCode0
Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation0
Semantic Segmentation via Highly Fused Convolutional Network with Multiple Soft Cost Functions0
Improving Depth Completion via Depth Feature Upsampling0
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