Negative Sample is Negative in Its Own Way: Tailoring Negative Sentences forImage-Text Retrieval

Related tags

Deep LearningTAGS
Overview

NSGDC

Some codes in this repo are copied/modified from opensource implementations made available by UNITER, PyTorch, HuggingFace, OpenNMT, and Nvidia. The image features are extracted using BUTD.

Requirements

This is following UNITER. We provide Docker image for easier reproduction. Please install the following:

Our scripts require the user to have the docker group membership so that docker commands can be run without sudo. We only support Linux with NVIDIA GPUs. We test on Ubuntu 18.04 and V100 cards. We use mixed-precision training hence GPUs with Tensor Cores are recommended.

Image-Text Retrieval

Download Data

bash scripts/download_itm.sh $PATH_TO_STORAGE

Launch the Docker Container

# docker image should be automatically pulled
source launch_container.sh $PATH_TO_STORAGE/txt_db $PATH_TO_STORAGE/img_db \
$PATH_TO_STORAGE/finetune $PATH_TO_STORAGE/pretrained

In case you would like to reproduce the whole preprocessing pipeline.

The launch script respects $CUDA_VISIBLE_DEVICES environment variable. Note that the source code is mounted into the container under /src instead of built into the image so that user modification will be reflected without re-building the image. (Data folders are mounted into the container separately for flexibility on folder structures.)

Image-Text Retrieval (Flickr30k)

# Train wit the base setting
bash run_cmds/tran_pnsgd_base_flickr.sh
bash run_cmds/tran_pnsgd2_base_flickr.sh

# Train wit the large setting
bash run_cmds/tran_pnsgd_large_flickr.sh
bash run_cmds/tran_pnsgd2_large_flickr.sh

Image-Text Retrieval (COCO)

# Train wit the base setting
bash run_cmds/tran_pnsgd_base_coco.sh
bash run_cmds/tran_pnsgd2_base_coco.sh

# Train wit the large setting
bash run_cmds/tran_pnsgd_large_coco.sh
bash run_cmds/tran_pnsgd2_large_coco.sh

Run Inference

bash run_cmds/inf_nsgd.sh

Results

Our models achieve the following performance.

MS-COCO

Model Image-to-Text Text-to-Image
[email protected] [email protected] [email protected] [email protected] [email protected] [email protected]
NSGDC-Base 66.6 88.6 94.0 51.6 79.1 87.5
NSGDC-Large 67.8 89.6 94.2 53.3 80.0 88.0

Flickr30K

Model Image-to-Text Text-to-Image
[email protected] [email protected] [email protected] [email protected] [email protected] [email protected]
NSGDC-Base 87.9 98.1 99.3 74.5 93.3 96.3
NSGDC-Large 90.6 98.8 99.1 77.3 94.3 97.3
Owner
Zhihao Fan
Zhihao Fan
Waymo motion prediction challenge 2021: 3rd place solution

Waymo motion prediction challenge 2021: 3rd place solution 📜 Technical report 🗨️ Presentation 🎉 Announcement 🛆Motion Prediction Channel Website 🛆

158 Jan 08, 2023
Adversarial Graph Augmentation to Improve Graph Contrastive Learning

ADGCL : Adversarial Graph Augmentation to Improve Graph Contrastive Learning Introduction This repo contains the Pytorch [1] implementation of Adversa

susheel suresh 62 Nov 19, 2022
EMNLP'2021: Simple Entity-centric Questions Challenge Dense Retrievers

EntityQuestions This repository contains the EntityQuestions dataset as well as code to evaluate retrieval results from the the paper Simple Entity-ce

Princeton Natural Language Processing 119 Sep 28, 2022
OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)

OCTIS : Optimizing and Comparing Topic Models is Simple! OCTIS (Optimizing and Comparing Topic models Is Simple) aims at training, analyzing and compa

MIND 478 Jan 01, 2023
LRBoost is a scikit-learn compatible approach to performing linear residual based stacking/boosting.

LRBoost is a sckit-learn compatible package for linear residual boosting. LRBoost combines a linear estimator and a non-linear estimator to leverage t

Andrew Patton 5 Nov 23, 2022
Reinforcement Learning for Portfolio Management

qtrader Reinforcement Learning for Portfolio Management Why Reinforcement Learning? Learns the optimal action, rather than models the market. Adaptive

Angelos Filos 406 Jan 01, 2023
LBK 35 Dec 26, 2022
Automated Attendance Project Using Face Recognition

dependencies for project: cmake 3.22.1 dlib 19.22.1 face-recognition 1.3.0 openc

Rohail Taha 1 Jan 09, 2022
Source Code For Template-Based Named Entity Recognition Using BART

Template-Based NER Source Code For Template-Based Named Entity Recognition Using BART Training Training train.py Inference inference.py Corpus ATIS (h

174 Dec 19, 2022
BRNet - code for Automated assessment of BI-RADS categories for ultrasound images using multi-scale neural networks with an order-constrained loss function

BRNet code for "Automated assessment of BI-RADS categories for ultrasound images using multi-scale neural networks with an order-constrained loss func

Yong Pi 2 Mar 09, 2022
Cleaned up code for DSTC 10: SIMMC 2.0 track: subtask 2: multimodal coreference resolution

UNITER-Based Situated Coreference Resolution with Rich Multimodal Input: arXiv MMCoref_cleaned Code for the MMCoref task of the SIMMC 2.0 dataset. Pre

Yichen (William) Huang 2 Dec 05, 2022
Source code for models described in the paper "AudioCLIP: Extending CLIP to Image, Text and Audio" (https://arxiv.org/abs/2106.13043)

AudioCLIP Extending CLIP to Image, Text and Audio This repository contains implementation of the models described in the paper arXiv:2106.13043. This

458 Jan 02, 2023
Source code for paper "Deep Superpixel-based Network for Blind Image Quality Assessment"

DSN-IQA Source code for paper "Deep Superpixel-based Network for Blind Image Quality Assessment" Requirements Python =3.8.0 Pytorch =1.7.1 Usage wit

7 Oct 13, 2022
ReLoss - Official implementation for paper "Relational Surrogate Loss Learning" ICLR 2022

Relational Surrogate Loss Learning (ReLoss) Official implementation for paper "R

Tao Huang 31 Nov 22, 2022
Multi-Modal Machine Learning toolkit based on PyTorch.

简体中文 | English TorchMM 简介 多模态学习工具包 TorchMM 旨在于提供模态联合学习和跨模态学习算法模型库,为处理图片文本等多模态数据提供高效的解决方案,助力多模态学习应用落地。 近期更新 2022.1.5 发布 TorchMM 初始版本 v1.0 特性 丰富的任务场景:工具

njustkmg 1 Jan 05, 2022
Syntax-Aware Action Targeting for Video Captioning

Syntax-Aware Action Targeting for Video Captioning Code for SAAT from "Syntax-Aware Action Targeting for Video Captioning" (Accepted to CVPR 2020). Th

59 Oct 13, 2022
Optimal space decomposition based-product quantization for approximate nearest neighbor search

Optimal space decomposition based-product quantization for approximate nearest neighbor search Abstract Product quantization(PQ) is an effective neare

Mylove 1 Nov 19, 2021
Official Repsoitory for "Activate or Not: Learning Customized Activation." [CVPR 2021]

CVPR 2021 | Activate or Not: Learning Customized Activation. This repository contains the official Pytorch implementation of the paper Activate or Not

184 Dec 27, 2022
Empower Sequence Labeling with Task-Aware Language Model

LM-LSTM-CRF Check Our New NER Toolkit 🚀 🚀 🚀 Inference: LightNER: inference w. models pre-trained / trained w. any following tools, efficiently. Tra

Liyuan Liu 838 Jan 05, 2023
Face detection using deep learning.

Face Detection Docker Solution Using Faster R-CNN Dockerface is a deep learning face detector. It deploys a trained Faster R-CNN network on Caffe thro

Nataniel Ruiz 181 Dec 19, 2022