SOTAVerified

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 2125 of 25 papers

TitleStatusHype
Improving Depth Completion via Depth Feature Upsampling0
CARAFE: Content-Aware ReAssembly of FEaturesCode0
Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation0
Semantic Segmentation via Highly Fused Convolutional Network with Multiple Soft Cost Functions0
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object DetectionCode0
Show:102550
← PrevPage 3 of 3Next →

No leaderboard results yet.