Probabilistic Cross-Modal Embedding (PCME) CVPR 2021

Related tags

Deep Learningpcme
Overview

Probabilistic Cross-Modal Embedding (PCME) CVPR 2021

Official Pytorch implementation of PCME | Paper

Sanghyuk Chun1 Seong Joon Oh1 Rafael Sampaio de Rezende2 Yannis Kalantidis2 Diane Larlus2

1NAVER AI LAB
2NAVER LABS Europe

VIDEO

Updates

  • 23 Jun, 2021: Initial upload.

Installation

Install dependencies using the following command.

pip install cython && pip install -r requirements.txt
python -c 'import nltk; nltk.download("punkt", download_dir="/opt/conda/nltk_data")'
git clone https://github.com/NVIDIA/apex && cd apex && pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

Dockerfile

You can use my docker image as well

docker pull sanghyukchun/pcme:torch1.2-apex-dali

Please Add --model__cache_dir /vector_cache when you run the code

Configuration

All experiments are based on configuration files (see config/coco and config/cub). If you want to change only a few options, instead of re-writing a new configuration file, you can override the configuration as the follows:

python .py --dataloader__batch_size 32 --dataloader__eval_batch_size 8 --model__eval_method matching_prob

See config/parser.py for details

Dataset preparation

COCO Caption

We followed the same split provided by VSE++. Dataset splits can be found in datasets/annotations.

Note that we also need instances_2014.json for computing PMRP score.

CUB Caption

Download images from this link, and download caption from reedscot/cvpr2016. You can use the image path and the caption path separately in the code.

Evaluate pretrained models

NOTE: the current implementation of plausible match R-Precision (PMRP) is not efficient:
It first dumps all ranked items for each item to a local file, and compute R-precision.
We are planning to re-implement efficient PMRP as soon as possible.

COCO Caption

# Compute recall metrics
python evaluate_recall_coco.py ./config/coco/pcme_coco.yaml \
    --dataset_root  \
    --model_path model_last.pth \
    # --model__cache_dir /vector_cache # if you use my docker image
# Compute plausible match R-Precision (PMRP) metric
python extract_rankings_coco.py ./config/coco/pcme_coco.yaml \
    --dataset_root  \
    --model_path model_last.pth \
    --dump_to  \
    # --model__cache_dir /vector_cache # if you use my docker image

python evaluate_pmrp_coco.py --ranking_file 
Method I2T PMRP I2T [email protected] T2I PMRP T2I [email protected] Model file
PCME 45.0 68.8 46.0 54.6 link
PVSE K=1 40.3 66.7 41.8 53.5 -
PVSE K=2 42.8 69.2 43.6 55.2 -
VSRN 41.2 76.2 42.4 62.8 -
VSRN + AOQ 44.7 77.5 45.6 63.5 -

CUB Caption

python evaluate_cub.py ./config/cub/pcme_cub.yaml \
    --dataset_root  \
    --caption_root  \
    --model_path model_last.pth \
    # --model__cache_dir /vector_cache # if you use my docker image

NOTE: If you just download file from reedscot/cvpr2016, then caption_root will be cvpr2016_cub/text_c10

If you want to test other probabilistic distances, such as Wasserstein distance or KL-divergence, try the following command:

python evaluate_cub.py ./config/cub/pcme_cub.yaml \
    --dataset_root  \
    --caption_root  \
    --model_path model_last.pth \
    --model__eval_method  \
    # --model__cache_dir /vector_cache # if you use my docker image

You can choose distance_method in ['elk', 'l2', 'min', 'max', 'wasserstein', 'kl', 'reverse_kl', 'js', 'bhattacharyya', 'matmul', 'matching_prob']

How to train

NOTE: we train each model with mixed-precision training (O2) on a single V100.
Since, the current code does not support multi-gpu training, if you use different hardware, the batchsize should be reduced.
Please note that, hence, the results couldn't be reproduced if you use smaller hardware than V100.

COCO Caption

python train_coco.py ./config/coco/pcme_coco.yaml --dataset_root  \
    # --model__cache_dir /vector_cache # if you use my docker image

It takes about 46 hours in a single V100 with mixed precision training.

CUB Caption

We use CUB Caption dataset (Reed, et al. 2016) as a new cross-modal retrieval benchmark. Here, instead of matching the sparse paired image-caption pairs, we treat all image-caption pairs in the same class as positive. Since our split is based on the zero-shot learning benchmark (Xian, et al. 2017), we leave out 50 classes from 200 bird classes for the evaluation.

  • Reed, Scott, et al. "Learning deep representations of fine-grained visual descriptions." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
  • Xian, Yongqin, Bernt Schiele, and Zeynep Akata. "Zero-shot learning-the good, the bad and the ugly." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017.

hyperparameter search

We additionally use cross-validation splits by (Xian, et el. 2017), namely using 100 classes for training and 50 classes for validation.

python train_cub.py ./config/cub/pcme_cub.yaml \
    --dataset_root  \
    --caption_root  \
    --dataset_name cub_trainval1 \
    # --model__cache_dir /vector_cache # if you use my docker image

Similarly, you can use cub_trainval2 and cub_trainval3 as well.

training with full training classes

python train_cub.py ./config/cub/pcme_cub.yaml \
    --dataset_root  \
    --caption_root  \
    # --model__cache_dir /vector_cache # if you use my docker image

It takes about 4 hours in a single V100 with mixed precision training.

How to cite

@inproceedings{chun2021pcme,
    title={Probabilistic Embeddings for Cross-Modal Retrieval},
    author={Chun, Sanghyuk and Oh, Seong Joon and De Rezende, Rafael Sampaio and Kalantidis, Yannis and Larlus, Diane},
    year={2021},
    booktitle={Conference on Computer Vision and Pattern Recognition (CVPR)},
}

License

MIT License

Copyright (c) 2021-present NAVER Corp.

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
Owner
NAVER AI
Official account of NAVER AI, Korea No.1 Industrial AI Research Group
NAVER AI
A rough implementation of the paper "A Steering Algorithm for Redirected Walking Using Reinforcement Learning"

A rough implementation of the paper "A Steering Algorithm for Redirected Walking Using Reinforcement Learning"

Somnus `Chen 2 Jun 09, 2022
UniFormer - official implementation of UniFormer

UniFormer This repo is the official implementation of "Uniformer: Unified Transf

SenseTime X-Lab 573 Jan 04, 2023
Efficient semidefinite bounds for multi-label discrete graphical models.

Low rank solvers #################################### benchmark/ : folder with the random instances used in the paper. ############################

1 Dec 08, 2022
fcn by tensorflow

Update An example on how to integrate this code into your own semantic segmentation pipeline can be found in my KittiSeg project repository. tensorflo

9 May 22, 2022
Learning from Synthetic Data with Fine-grained Attributes for Person Re-Identification

Less is More: Learning from Synthetic Data with Fine-grained Attributes for Person Re-Identification Suncheng Xiang Shanghai Jiao Tong University Over

SunchengXiang 68 Dec 13, 2022
Pytorch Geometric Tutorials

Pytorch Geometric Tutorials

Antonio Longa 648 Jan 08, 2023
Poplar implementation of "Bundle Adjustment on a Graph Processor" (CVPR 2020)

Poplar Implementation of Bundle Adjustment using Gaussian Belief Propagation on Graphcore's IPU Implementation of CVPR 2020 paper: Bundle Adjustment o

Joe Ortiz 34 Dec 05, 2022
A data-driven maritime port simulator

PySeidon - A Data-Driven Maritime Port Simulator 🌊 Extendable and modular software for maritime port simulation. This software uses entity-component

6 Apr 10, 2022
Data and extra materials for the food safety publications classifier

Data and extra materials for the food safety publications classifier The subdirectories contain detailed descriptions of their contents in the README.

1 Jan 20, 2022
Yoga - Yoga asana classifier for python

Yoga Asana Classifier Description Hi welcome to my new deep learning project "Yo

Programminghut 35 Dec 12, 2022
Implementation of CSRL from the AAAI2022 paper: Constraint Sampling Reinforcement Learning: Incorporating Expertise For Faster Learning

CSRL Implementation of CSRL from the AAAI2022 paper: Constraint Sampling Reinforcement Learning: Incorporating Expertise For Faster Learning Python: 3

4 Apr 14, 2022
Scripts and misc. stuff related to the PortSwigger Web Academy

PortSwigger Web Academy Notes Mostly scripts to automate the exploits. Going in the order of the recomended learning path - starting with SQLi. Commun

pageinsec 17 Dec 30, 2022
Deep Learning Emotion decoding using EEG data from Autism individuals

Deep Learning Emotion decoding using EEG data from Autism individuals This repository includes the python and matlab codes using for processing EEG 2D

Juan Manuel Mayor Torres 12 Dec 08, 2022
SCALE: Modeling Clothed Humans with a Surface Codec of Articulated Local Elements (CVPR 2021)

SCALE: Modeling Clothed Humans with a Surface Codec of Articulated Local Elements (CVPR 2021) This repository contains the official PyTorch implementa

Qianli Ma 133 Jan 05, 2023
An Api for Emotion recognition.

PLAYEMO Playemo was built from the ground-up with Flask, a python tool that makes it easy for developers to build APIs. Use Cases Is Python your langu

greek geek 2 Jul 16, 2022
Pairwise model for commonlit competition

Pairwise model for commonlit competition To run: - install requirements - create input directory with train_folds.csv and other competition data - cd

abhishek thakur 45 Aug 31, 2022
FewBit — a library for memory efficient training of large neural networks

FewBit FewBit — a library for memory efficient training of large neural networks. Its efficiency originates from storage optimizations applied to back

24 Oct 22, 2022
Subdivision-based Mesh Convolutional Networks

Subdivision-based Mesh Convolutional Networks The official implementation of SubdivNet in our paper, Subdivion-based Mesh Convolutional Networks Requi

Zheng-Ning Liu 181 Dec 28, 2022
NIMA: Neural IMage Assessment

PyTorch NIMA: Neural IMage Assessment PyTorch implementation of Neural IMage Assessment by Hossein Talebi and Peyman Milanfar. You can learn more from

Kyryl Truskovskyi 293 Dec 30, 2022
Interactive Visualization to empower domain experts to align ML model behaviors with their knowledge.

An interactive visualization system designed to helps domain experts responsibly edit Generalized Additive Models (GAMs). For more information, check

InterpretML 83 Jan 04, 2023