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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
JAFAR: Jack up Any Feature at Any ResolutionCode3
Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive SegmentationCode0
LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation ModelsCode3
LDA-AQU: Adaptive Query-guided Upsampling via Local Deformable AttentionCode0
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
A Refreshed Similarity-based Upsampler for Direct High-Ratio Feature UpsamplingCode0
LiFT: A Surprisingly Simple Lightweight Feature Transform for Dense ViT DescriptorsCode0
FeatUp: A Model-Agnostic Framework for Features at Any ResolutionCode5
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