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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
ASCNet: Asymmetric Sampling Correction Network for Infrared Image DestripingCode2
Improving Depth Completion via Depth Feature Upsampling0
Learning to Upsample by Learning to SampleCode1
On Point Affiliation in Feature UpsamplingCode1
SeaFormer++: Squeeze-enhanced Axial Transformer for Mobile Visual RecognitionCode2
SAPA: Similarity-Aware Point Affiliation for Feature UpsamplingCode1
FADE: Fusing the Assets of Decoder and Encoder for Task-Agnostic UpsamplingCode1
Local and Global GANs with Semantic-Aware Upsampling for Image GenerationCode1
Deep ViT Features as Dense Visual DescriptorsCode1
Joint Denoising and Demosaicking with Green Channel Prior for Real-world Burst ImagesCode1
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