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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 1120 of 25 papers

TitleStatusHype
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
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