Cross-modal Retrieval using Transformer Encoder Reasoning Networks (TERN). With use of Metric Learning and FAISS for fast similarity search on GPU

Overview

Cross-modal Retrieval using Transformer Encoder Reasoning Networks

This project reimplements the idea from "Transformer Reasoning Network for Image-Text Matching and Retrieval". To solve the task of cross-modal retrieval, representative features from both modal are extracted using distinctive pipeline and then projected into the same embedding space. Because the features are sequence of vectors, Transformer-based model can be utilised to work best. In this repo, my highlight contribution is:

  • Reimplement TERN module, which exploits the effectiveness of using Transformer on bottom-up attention features and bert features.
  • Take advantage of facebookresearch's FAISS for efficient similarity search and clustering of dense vectors.
  • Experiment various metric learning loss objectives from KevinMusgrave's Pytorch Metric Learning

The figure below shows the overview of the architecture

screen

Datasets

  • I trained TERN on Flickr30k dataset which contains 31,000 images collected from Flickr, together with 5 reference sentences provided by human annotators for each image. For each sample, visual and text features are pre-extracted as numpy files

  • Some samples from the dataset:

Images Captions
screen 1. An elderly man is setting the table in front of an open door that leads outside to a garden.
2. The guy in the black sweater is looking onto the table below.
3. A man in a black jacket picking something up from a table.
4. An old man wearing a black jacket is looking on the table.
5. The gray-haired man is wearing a sweater.
screen 1. Two men are working on a bicycle on the side of the road.
2. Three men working on a bicycle on a cobblestone street.
3. Two men wearing shorts are working on a blue bike.
4. Three men inspecting a bicycle on a street.
5. Three men examining a bicycle.

Execution

  • Installation
pip install -r requirements.txt
apt install libomp-dev
pip install faiss-gpu
  • Specify dataset paths and configuration in the config file

  • For training

PYTHONPATH=. python tools/train.py 
  • For evaluation
PYTHONPATH=. python tools/eval.py \
                --top_k= <top k similarity> \
                --weight= <model checkpoint> \

Notebooks

  • Notebook Inference TERN on Flickr30k dataset
  • Notebook Use FasterRCNN to extract Bottom Up embeddings
  • Notebook Use BERT to extract text embeddings

Results

  • Validation m on Flickr30k dataset (trained for 100 epochs):
Model Weights i2t/[email protected] t2i/[email protected]
TERN link 0.5174 0.7496
  • Some visualization
Query text: Two dogs are running along the street
screen
Query text: The woman is holding a violin
screen
Query text: Young boys are playing baseball
screen
Query text: A man is standing, looking at a lake
screen

Paper References

@misc{messina2021transformer,
      title={Transformer Reasoning Network for Image-Text Matching and Retrieval}, 
      author={Nicola Messina and Fabrizio Falchi and Andrea Esuli and Giuseppe Amato},
      year={2021},
      eprint={2004.09144},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
@misc{anderson2018bottomup,
      title={Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering}, 
      author={Peter Anderson and Xiaodong He and Chris Buehler and Damien Teney and Mark Johnson and Stephen Gould and Lei Zhang},
      year={2018},
      eprint={1707.07998},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
@article{JDH17,
  title={Billion-scale similarity search with GPUs},
  author={Johnson, Jeff and Douze, Matthijs and J{\'e}gou, Herv{\'e}},
  journal={arXiv preprint arXiv:1702.08734},
  year={2017}
}

Code References

Owner
Minh-Khoi Pham
Passionate Machine Learner
Minh-Khoi Pham
Semantic Segmentation with Pytorch-Lightning

This is a simple demo for performing semantic segmentation on the Kitti dataset using Pytorch-Lightning and optimizing the neural network by monitoring and comparing runs with Weights & Biases.

Boris Dayma 58 Nov 18, 2022
A Dataset of Python Challenges for AI Research

Python Programming Puzzles (P3) This repo contains a dataset of python programming puzzles which can be used to teach and evaluate an AI's programming

Microsoft 850 Dec 24, 2022
The final project of "Applying AI to EHR Data" of "AI for Healthcare" nanodegree - Udacity.

Patient Selection for Diabetes Drug Testing Project Overview EHR data is becoming a key source of real-world evidence (RWE) for the pharmaceutical ind

Omar Laham 1 Jan 14, 2022
A Real-ESRGAN equipped Colab notebook for CLIP Guided Diffusion

#360Diffusion automatically upscales your CLIP Guided Diffusion outputs using Real-ESRGAN. Latest Update: Alpha 1.61 [Main Branch] - 01/11/22 Layout a

78 Nov 02, 2022
A naive ROS interface for visualDet3D.

YOLO3D ROS Node This repo contains a Monocular 3D detection Ros node. Base on https://github.com/Owen-Liuyuxuan/visualDet3D All parameters are exposed

Yuxuan Liu 19 Oct 08, 2022
NeuroFind - A solution to the to the Task given by the Oberseminar of Messtechnik Institute of TU Dresden in 2021

NeuroFind A solution to the to the Task given by the Oberseminar of Messtechnik

1 Jan 20, 2022
Time Series Forecasting with Temporal Fusion Transformer in Pytorch

Forecasting with the Temporal Fusion Transformer Multi-horizon forecasting often contains a complex mix of inputs – including static (i.e. time-invari

Nicolás Fornasari 6 Jan 24, 2022
Official Pytorch and JAX implementation of "Efficient-VDVAE: Less is more"

The Official Pytorch and JAX implementation of "Efficient-VDVAE: Less is more" Arxiv preprint Louay Hazami   ·   Rayhane Mama   ·   Ragavan Thurairatn

Rayhane Mama 144 Dec 23, 2022
Auto White-Balance Correction for Mixed-Illuminant Scenes

Auto White-Balance Correction for Mixed-Illuminant Scenes Mahmoud Afifi, Marcus A. Brubaker, and Michael S. Brown York University Video Reference code

Mahmoud Afifi 47 Nov 26, 2022
Face recognize and crop them

Face Recognize Cropping Module Source 아이디어 Face Alignment with OpenCV and Python Requirement 필요 라이브러리 imutil dlib python-opence (cv2) Usage 사용 방법 open

Cho Moon Gi 1 Feb 15, 2022
Kaggle Feedback Prize - Evaluating Student Writing 15th solution

Kaggle Feedback Prize - Evaluating Student Writing 15th solution First of all, I would like to thank the excellent notebooks and discussions from http

Lingyuan Zhang 6 Mar 24, 2022
Self-Adaptable Point Processes with Nonparametric Time Decays

NPPDecay This is our implementation for the paper Self-Adaptable Point Processes with Nonparametric Time Decays, by Zhimeng Pan, Zheng Wang, Jeff M. P

zpan 2 Sep 24, 2022
Companion code for the paper Theoretical characterization of uncertainty in high-dimensional linear classification

Companion code for the paper Theoretical characterization of uncertainty in high-dimensional linear classification Usage The required packages are lis

0 Feb 07, 2022
Code release to accompany paper "Geometry-Aware Gradient Algorithms for Neural Architecture Search."

Geometry-Aware Gradient Algorithms for Neural Architecture Search This repository contains the code required to run the experiments for the DARTS sear

18 May 27, 2022
TransReID: Transformer-based Object Re-Identification

TransReID: Transformer-based Object Re-Identification [arxiv] The official repository for TransReID: Transformer-based Object Re-Identification achiev

569 Dec 30, 2022
AAAI-22 paper: SimSR: Simple Distance-based State Representationfor Deep Reinforcement Learning

SimSR Code and dataset for the paper SimSR: Simple Distance-based State Representationfor Deep Reinforcement Learning (AAAI-22). Requirements We assum

7 Dec 19, 2022
Implementation of SegNet: A Deep Convolutional Encoder-Decoder Architecture for Semantic Pixel-Wise Labelling

Caffe SegNet This is a modified version of Caffe which supports the SegNet architecture As described in SegNet: A Deep Convolutional Encoder-Decoder A

Alex Kendall 1.1k Jan 02, 2023
House3D: A Rich and Realistic 3D Environment

House3D: A Rich and Realistic 3D Environment Yi Wu, Yuxin Wu, Georgia Gkioxari and Yuandong Tian House3D is a virtual 3D environment which consists of

Meta Research 1.1k Dec 14, 2022
This repository contains project created during the Data Challenge module at London School of Hygiene & Tropical Medicine

LSHTM_RCS This repository contains project created during the Data Challenge module at London School of Hygiene & Tropical Medicine (LSHTM) in collabo

Lukas Kopecky 3 Jan 30, 2022
The MLOps platform for innovators 🚀

​ DS2.ai is an integrated AI operation solution that supports all stages from custom AI development to deployment. It is an AI-specialized platform service that collects data, builds a training datas

9 Jan 03, 2023