Papers with Code 아카이브

Image Matching

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

4

평가표

2

보존된 평가 기록

18

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/seungryong/FCSS

FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence

https://github.com/seungryong/RTNs

Recurrent Transformer Networks for Semantic Correspondence

https://github.com/shepnerd/AttenNorm

Attentive Normalization for Conditional Image Generation

https://github.com/shiramir/dino-vit-features

Deep ViT Features as Dense Visual Descriptors

https://github.com/shuaiyihuang/scorrsan

Learning Semantic Correspondence with Sparse Annotations

https://github.com/siyuanliii/masa

DETRs with Collaborative Hybrid Assignments Training

https://github.com/snap-stanford/neural-subgraph-learning-gnn

Convolutional Set Matching for Graph Similarity

https://github.com/snowfallingplum/csd-mt

SSAT: A Symmetric Semantic-Aware Transformer Network for Makeup Transfer and Removal

https://github.com/snowfallingplum/shmt

SSAT: A Symmetric Semantic-Aware Transformer Network for Makeup Transfer and Removal

https://github.com/tzvikif/SuperGlue

SuperGlue: Learning Feature Matching with Graph Neural Networks

https://github.com/ubc-vision/image-matching-benchmark

https://github.com/ucuapps/openglue

SuperGlue: Learning Feature Matching with Graph Neural Networks

https://github.com/val-iisc/pose_estimation

iSPA-Net: Iterative Semantic Pose Alignment Network

https://github.com/vdvchen/sgmnet

SuperGlue: Learning Feature Matching with Graph Neural Networks

https://github.com/visinf/cad

D2-Net: A Trainable CNN for Joint Detection and Description of Local Features

https://github.com/voidrank/DiscoBox

DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision

https://github.com/voidrank/SCOT

DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision

https://github.com/wangermeng2021/SnapMix-tensorflow2

SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data

https://github.com/williamyang1991/rerender_a_video

Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation

https://github.com/xlearning-scu/2023-iccv-common

SuperGlue: Learning Feature Matching with Graph Neural Networks

https://github.com/yangyucheng000/University/tree/main/model-2/esm

ESM+: Modern Insights into Perspective on Text-to-SQL Evaluation in the Age of Large Language Models

https://github.com/zhaohengyuan1/Color2Embed

Color2Embed: Fast Exemplar-Based Image Colorization using Color Embeddings

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.