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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 110 of 25 papers

TitleStatusHype
FeatUp: A Model-Agnostic Framework for Features at Any ResolutionCode5
JAFAR: Jack up Any Feature at Any ResolutionCode3
LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation ModelsCode3
ASCNet: Asymmetric Sampling Correction Network for Infrared Image DestripingCode2
SeaFormer++: Squeeze-enhanced Axial Transformer for Mobile Visual RecognitionCode2
Lighten CARAFE: Dynamic Lightweight Upsampling with Guided Reassemble KernelsCode1
EfficientCD: A New Strategy For Change Detection Based With Bi-temporal Layers ExchangedCode1
FADE: A Task-Agnostic Upsampling Operator for Encoder-Decoder ArchitecturesCode1
Learning to Upsample by Learning to SampleCode1
On Point Affiliation in Feature UpsamplingCode1
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