”correspondence“ 的搜索结果

Code for "Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation"

Multiple correspondence analysis

Unsupervised Semantic Segmentation by Distilling Feature Correspondences

Code release for "learning to find good correspondences" CVPR 2018

Learning Correspondence from the Cycle-consistency of Time (CVPR 2019)

翻译 - 从时间周期一致性中学习对应性（CVPR 2019）

[NeurIPS'23] Emergent Correspondence from Image Diffusion

GMS: Grid-based Motion Statistics for Fast, Ultra-robust Feature Correspondence (CVPR 17 & IJCV 20)

[CVPR2024] StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On

[CVPR 2024] FRESCO: Spatial-Temporal Correspondence for Zero-Shot Video Translation

Fully Convolutional Geometric Features: Fast and accurate 3D features for registration and correspondence.

Volumetric Correspondence Networks for Optical Flow, NeurIPS 2019.

Self-supervised Learning for Video Correspondence Flow (BMVC 2019)

翻译 - 视频函授流程的自我监督学习（BMVC 2019）

Pipeline for putting surface meshes into correspondence

Learning Video Representations from Correspondence Proposals (CVPR 2019 Oral)

A PyTorch implementation of "DGC-Net: Dense Geometric Correspondence Network"

翻译 - “ DGC-Net：密集几何通信网络”的PyTorch实现

[TPAMI 2020] Parallax Attention for Unsupervised Stereo Correspondence Learning

Joint-task Self-supervised Learning for Temporal Correspondence (NeurIPS 2019)

Code release for "COTR: Correspondence Transformer for Matching Across Images"(ICCV 2021)

Repository for "Space-Time Correspondence as a Contrastive Random Walk" (NeurIPS 2020)

LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimation, ECCV 2020

CVPR 2020, Semantic Correspondence as an Optimal Transport Problem, Pytorch Implementation.

PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction

genotype :: ped correspondence check, ancestry check, sex check. directly, quickly on VCF

Efficient binocular stereo correspondence matching with 1-D Max-Trees