This is the repository for CVPR2021 Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales

Related tags

Deep Learning DynamicMetricLearning
Overview
You might also like...
PyTorch implementation of paper
PyTorch implementation of paper "Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes", CVPR 2021

Neural Scene Flow Fields PyTorch implementation of paper "Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes", CVPR 20

Zhengqi Li 585 Jan 4, 2023
PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in clustering (CVPR2021)
PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in clustering (CVPR2021)

PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering Jang Hyun Cho1, Utkarsh Mall2, Kavita Bala2, Bharath Harihar

Jang Hyun Cho 164 Dec 30, 2022
(CVPR2021) DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation
(CVPR2021) DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation

DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation CVPR2021(oral) [arxiv] Requirements python3.7 pytorch==

W-zx-Y 85 Dec 7, 2022
Dynamic View Synthesis from Dynamic Monocular Video

Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer This repository contains code to compute depth from a

Intelligent Systems Lab Org 2.3k Jan 1, 2023
Dynamic View Synthesis from Dynamic Monocular Video
Dynamic View Synthesis from Dynamic Monocular Video

Dynamic View Synthesis from Dynamic Monocular Video Project Website | Video | Paper Dynamic View Synthesis from Dynamic Monocular Video Chen Gao, Ayus

Chen Gao 139 Dec 28, 2022
Dynamic vae - Dynamic VAE algorithm is used for anomaly detection of battery data
Dynamic vae - Dynamic VAE algorithm is used for anomaly detection of battery data

Dynamic VAE frame Automatic feature extraction can be achieved by probability di

null 10 Oct 7, 2022
Object detection on multiple datasets with an automatically learned unified label space.
Object detection on multiple datasets with an automatically learned unified label space.

Simple multi-dataset detection An object detector trained on multiple large-scale datasets with a unified label space; Winning solution of E

Xingyi Zhou 407 Dec 30, 2022
Auto Seg-Loss: Searching Metric Surrogates for Semantic Segmentation
Auto Seg-Loss: Searching Metric Surrogates for Semantic Segmentation

Auto-Seg-Loss By Hao Li, Chenxin Tao, Xizhou Zhu, Xiaogang Wang, Gao Huang, Jifeng Dai This is the official implementation of the ICLR 2021 paper Auto

null 61 Dec 21, 2022
Facial Action Unit Intensity Estimation via Semantic Correspondence Learning with Dynamic Graph Convolution
Facial Action Unit Intensity Estimation via Semantic Correspondence Learning with Dynamic Graph Convolution

FAU Implementation of the paper: Facial Action Unit Intensity Estimation via Semantic Correspondence Learning with Dynamic Graph Convolution. Yingruo

Evelyn 78 Nov 29, 2022
Comments
  • Evaluation protocol & Pretrained Models

    Evaluation protocol & Pretrained Models

    Hi !

    I am working with your datasets.

    I have some more questions, do you plan on releasing :

    • Training protocol (optimizer, pretrain or not resnet34, mining code...)
    • Hyperparameters used in your paper (ie. learning rates, hyperparameters for the loss etc.)
    • The evaluation protocol / scripts ?
    • Pre-trained models ?

    Thanks !

    opened by elias-ramzi 0
  • Questions regarding the datasets

    Questions regarding the datasets

    Hi thank you for your work making the datasets public !

    I have those questions regarding the datasets :

    • Are the query sets always included in the gallery sets ?
    • What would be the easiest way to build a test dataset with all the unique images and all their labels ?

    Thank you :)

    opened by elias-ramzi 0
  • 评价指标

    评价指标

    您好，我最近在阅读论文和复现Multi-scale learning baseline时对论文中所采用的评价指标有几点迷惑，还望解答! 首先说一下我的实验设置：

    1. 数据组织按照您所公布的源代码，backbone采用resnet34（imagenet pretrained）。
    2. 我目前只在Multi-Similarity Loss上进行了测试。因为每个batch中fine、middle、coarse的图像各占1/3，在某个scale下进行损失计算时，我只选择当前scale的1/3图像作为anchor来组织样本对。如假设batch-size为1，那么一个batch中有K*3张图片，前K张为fine-scale下采样出来的图片，那么在fine上进行损失函数计算时，我只使用K张图片作为anchor，negative的话则在整个bath上选择。
    3. 训练时batch-size我设置的为5， MS Loss的学习率等参数按照作者源代码中进行设置。
    4. 评价指标目前只使用了Recall@K，与MS Loss中相同。计算方式为3个尺度分别计算Recall@K，求平均。

    下面说一下我遇到的问题：

    1. 目前只在Vechicle上进行测试，无法获得论文中MS Loss下的实验效果（我获得的Recall@1， Recall@10都要高。
    2. 评价指标ASI中需要指定k，论文中公布的ASI指标是在k为多少时得到的。

    感谢您的查看，期待您的指教和解答！

    opened by SuQinghang 2
Owner
null
GitHub
[CVPR2021] The source code for our paper 《Removing the Background by Adding the Background: Towards Background Robust Self-supervised Video Representation Learning》.

TBE The source code for our paper "Removing the Background by Adding the Background: Towards Background Robust Self-supervised Video Representation Le

Jinpeng Wang 150 Dec 28, 2022
PyTorch implementation of our Adam-NSCL algorithm from our CVPR2021 (oral) paper "Training Networks in Null Space for Continual Learning"

Adam-NSCL This is a PyTorch implementation of Adam-NSCL algorithm for continual learning from our CVPR2021 (oral) paper: Title: Training Networks in N

Shipeng Wang 34 Dec 21, 2022
Towards Interpretable Deep Metric Learning with Structural Matching

DIML Created by Wenliang Zhao*, Yongming Rao*, Ziyi Wang, Jiwen Lu, Jie Zhou This repository contains PyTorch implementation for paper Towards Interpr

Wenliang Zhao 75 Nov 11, 2022
Official implementation of "Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets" (CVPR2021)

Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets This is the official implementation of "Towards Good Pract

Sanja Fidler's Lab 52 Nov 22, 2022
[CVPR2021] DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets

DoDNet This repo holds the pytorch implementation of DoDNet: DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datase

null 116 Dec 12, 2022
Source code and Dataset creation for the paper "Neural Symbolic Regression That Scales"

NeuralSymbolicRegressionThatScales Pytorch implementation and pretrained models for the paper "Neural Symbolic Regression That Scales", presented at I

null 35 Nov 25, 2022
A modular, research-friendly framework for high-performance and inference of sequence models at many scales

T5X T5X is a modular, composable, research-friendly framework for high-performance, configurable, self-service training, evaluation, and inference of

Google Research 1.1k Jan 8, 2023
The official implementation of the CVPR2021 paper: Decoupled Dynamic Filter Networks

Decoupled Dynamic Filter Networks This repo is the official implementation of CVPR2021 paper: "Decoupled Dynamic Filter Networks". Introduction DDF is

F.S.Fire 180 Dec 30, 2022
FuseDream: Training-Free Text-to-Image Generationwith Improved CLIP+GAN Space OptimizationFuseDream: Training-Free Text-to-Image Generationwith Improved CLIP+GAN Space Optimization

FuseDream This repo contains code for our paper (paper link): FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimizat

XCL 191 Dec 31, 2022
Space robot - (Course Project) Using the space robot to capture the target satellite that is disabled and spinning, then stabilize and fix it up

Space robot - (Course Project) Using the space robot to capture the target satellite that is disabled and spinning, then stabilize and fix it up

Mingrui Yu 3 Jan 7, 2022