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Feature Upsampling
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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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation
LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models
LDA-AQU: Adaptive Query-guided Upsampling via Local Deformable Attention
Lighten CARAFE: Dynamic Lightweight Upsampling with Guided Reassemble Kernels
EfficientCD: A New Strategy For Change Detection Based With Bi-temporal Layers Exchanged
FADE: A Task-Agnostic Upsampling Operator for Encoder-Decoder Architectures
A Refreshed Similarity-based Upsampler for Direct High-Ratio Feature Upsampling
LiFT: A Surprisingly Simple Lightweight Feature Transform for Dense ViT Descriptors
FeatUp: A Model-Agnostic Framework for Features at Any Resolution
ASCNet: Asymmetric Sampling Correction Network for Infrared Image Destriping
Learning to Upsample by Learning to Sample
On Point Affiliation in Feature Upsampling
SeaFormer++: Squeeze-enhanced Axial Transformer for Mobile Visual Recognition
SAPA: Similarity-Aware Point Affiliation for Feature Upsampling
FADE: Fusing the Assets of Decoder and Encoder for Task-Agnostic Upsampling
Local and Global GANs with Semantic-Aware Upsampling for Image Generation
Deep ViT Features as Dense Visual Descriptors
Joint Denoising and Demosaicking with Green Channel Prior for Real-world Burst Images
CARAFE: Content-Aware ReAssembly of FEatures
Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation
Semantic Segmentation via Highly Fused Convolutional Network with Multiple Soft Cost Functions
Deep Image Prior
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.