https://github.com/GuoShi28/GCP-Net
공식 코드Joint Denoising and Demosaicking with Green Channel Prior for Real-world Burst Images
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Joint Denoising and Demosaicking with Green Channel Prior for Real-world Burst Images
Local and Global GANs with Semantic-Aware Upsampling for Image Generation
LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models
LDA-AQU: Adaptive Query-guided Upsampling via Local Deformable Attention
EfficientCD: A New Strategy For Change Detection Based With Bi-temporal Layers Exchanged
Lighten CARAFE: Dynamic Lightweight Upsampling with Guided Reassemble Kernels
SeaFormer++: Squeeze-enhanced Axial Transformer for Mobile Visual Recognition
Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation
FeatUp: A Model-Agnostic Framework for Features at Any Resolution
FADE: A Task-Agnostic Upsampling Operator for Encoder-Decoder Architectures
SAPA: Similarity-Aware Point Affiliation for Feature Upsampling
ASCNet: Asymmetric Sampling Correction Network for Infrared Image Destriping
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
A Refreshed Similarity-based Upsampler for Direct High-Ratio Feature Upsampling
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.