Oscar and VinVL

Overview

Oscar: Object-Semantics Aligned Pre-training for Vision-and-Language Tasks

VinVL: Revisiting Visual Representations in Vision-Language Models

Updates

05/28/2020: Released finetuned models on downstream tasks, please check MODEL_ZOO.md.
05/15/2020: Released pretrained models, datasets, and code for downstream tasks finetuning.
01/13/2021: our new work VinVL proposed OSCAR+, an improved version of OSCAR, and provided a better object-attribute detection model to extract features for V+L tasks. The VinVL work achieved SOTA performance on all seven V+L tasks here. Please stay tuned for the model and code release.
03/08/2021: Oscar+ pretraining code released, please check the last section in VinVL_MODEL_ZOO.md. All image features and model checkpoints in VinVL are also released. Please check VinVL for details.
04/13/2021: Our Scene Graph Benchmark Repo has been released. Welcome to use the code there to extract image features with VinVL pretrained models.

Introduction

This repository contains source code necessary to reproduce the results presented in the paper Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks. We propose a new cross-modal pre-training method Oscar (Object-Semantics Aligned Pre-training). It leverages object tags detected in images as anchor points to significantly ease the learning of image-text alignments. We pre-train Oscar on the public corpus of 6.5 million text-image pairs, and fine-tune it on downstream tasks, creating new state-of-the-arts on six well-established vision-language understanding and generation tasks. For more on this project, see the Microsoft Research Blog post.

Performance

Task t2i t2i i2t i2t IC IC IC IC NoCaps NoCaps VQA NLVR2 GQA
Metric [email protected] [email protected] [email protected] [email protected] [email protected] M C S C S test-std test-P test-std
SoTA_S 39.2 68.0 56.6 84.5 38.9 29.2 129.8 22.4 61.5 9.2 70.92 58.80 63.17
SoTA_B 54.0 80.8 70.0 91.1 40.5 29.7 137.6 22.8 86.58 12.38 73.67 79.30 -
SoTA_L 57.5 82.8 73.5 92.2 41.7 30.6 140.0 24.5 - - 74.93 81.47 -
----- --- --- --- --- --- --- --- --- --- --- --- --- ---
Oscar_B 54.0 80.8 70.0 91.1 40.5 29.7 137.6 22.8 78.8 11.7 73.44 78.36 61.62
Oscar_L 57.5 82.8 73.5 92.2 41.7 30.6 140.0 24.5 80.9 11.3 73.82 80.05 -
----- --- --- --- --- --- --- --- --- --- --- --- --- ---
VinVL_B 58.1 83.2 74.6 92.6 40.9 30.9 140.6 25.1 92.46 13.07 76.12 83.08 64.65
VinVL_L 58.8 83.5 75.4 92.9 41.0 31.1 140.9 25.2 - - 76.62 83.98 -
gain 1.3 0.7 1.9 0.6 -0.7 0.5 0.9 0.7 5.9 0.7 1.69 2.51 1.48

t2i: text-to-image retrieval; i2t: image-to-text retrieval; IC: image captioning on COCO.

Download

We released pre-trained models, datasets, VinVL image features, and Oscar+ pretraining corpus for downstream tasks. Please check VinVL_DOWNLOAD.md for details.

To download checkpoints for the Vanilla OSCAR, please check DOWNLOAD.md for details.

Installation

Check INSTALL.md for installation instructions.

Model Zoo

Check MODEL_ZOO.md for scripts to run oscar downstream finetuning.

Check VinVL_MODEL_ZOO.md for scripts to run oscar+ pretraining and downstream finetuning.

Citations

Please consider citing this paper if you use the code:

@article{li2020oscar,
  title={Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks},
  author={Li, Xiujun and Yin, Xi and Li, Chunyuan and Hu, Xiaowei and Zhang, Pengchuan and Zhang, Lei and Wang, Lijuan and Hu, Houdong and Dong, Li and Wei, Furu and Choi, Yejin and Gao, Jianfeng},
  journal={ECCV 2020},
  year={2020}
}

@article{zhang2021vinvl,
  title={VinVL: Making Visual Representations Matter in Vision-Language Models},
  author={Zhang, Pengchuan and Li, Xiujun and Hu, Xiaowei and Yang, Jianwei and Zhang, Lei and Wang, Lijuan and Choi, Yejin and Gao, Jianfeng},
  journal={CVPR 2021},
  year={2021}
}

License

Oscar is released under the MIT license. See LICENSE for details.

Owner
Microsoft
Open source projects and samples from Microsoft
Microsoft
The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL), NeurIPS-2021

Directed Graph Contrastive Learning The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL). In this paper, we present the first con

Tong Zekun 28 Jan 08, 2023
PushForKiCad - AISLER Push for KiCad EDA

AISLER Push for KiCad Push your layout to AISLER with just one click for instant

AISLER 31 Dec 29, 2022
Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation

Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation Prerequisites This repo is built upon a local copy of transfo

Jixuan Wang 10 Sep 28, 2022
Experiments on Flood Segmentation on Sentinel-1 SAR Imagery with Cyclical Pseudo Labeling and Noisy Student Training

Flood Detection Challenge This repository contains code for our submission to the ETCI 2021 Competition on Flood Detection (Winning Solution #2). Acco

Siddha Ganju 108 Dec 28, 2022
UV matrix decompostion using movielens dataset

UV-matrix-decompostion-with-kfold UV matrix decompostion using movielens dataset upload the 'ratings.dat' file install the following python libraries

2 Oct 18, 2022
A Framework for Encrypted Machine Learning in TensorFlow

TF Encrypted is a framework for encrypted machine learning in TensorFlow. It looks and feels like TensorFlow, taking advantage of the ease-of-use of t

TF Encrypted 0 Jul 06, 2022
ZSL-KG is a general-purpose zero-shot learning framework with a novel transformer graph convolutional network (TrGCN) to learn class representation from common sense knowledge graphs.

ZSL-KG is a general-purpose zero-shot learning framework with a novel transformer graph convolutional network (TrGCN) to learn class representa

Bats Research 94 Nov 21, 2022
Code implementation of Data Efficient Stagewise Knowledge Distillation paper.

Data Efficient Stagewise Knowledge Distillation Table of Contents Data Efficient Stagewise Knowledge Distillation Table of Contents Requirements Image

IvLabs 112 Dec 02, 2022
Learning from Synthetic Humans, CVPR 2017

Learning from Synthetic Humans (SURREAL) Gül Varol, Javier Romero, Xavier Martin, Naureen Mahmood, Michael J. Black, Ivan Laptev and Cordelia Schmid,

Gul Varol 538 Dec 18, 2022
DeLag: Detecting Latency Degradation Patterns in Service-based Systems

DeLag: Detecting Latency Degradation Patterns in Service-based Systems Replication package of the work "DeLag: Detecting Latency Degradation Patterns

SEALABQualityGroup @ University of L'Aquila 2 Mar 24, 2022
basic tutorial on pytorch

Quick Tutorial on PyTorch PyTorch Basics Linear Regression Logistic Regression Artificial Neural Networks Convolutional Neural Networks Recurrent Neur

7 Sep 15, 2022
Pseudo-Visual Speech Denoising

Pseudo-Visual Speech Denoising This code is for our paper titled: Visual Speech Enhancement Without A Real Visual Stream published at WACV 2021. Autho

Sindhu 94 Oct 22, 2022
Ensembling Off-the-shelf Models for GAN Training

Data-Efficient GANs with DiffAugment project | paper | datasets | video | slides Generated using only 100 images of Obama, grumpy cats, pandas, the Br

MIT HAN Lab 1.2k Dec 26, 2022
TensorFlow tutorials and best practices.

Effective TensorFlow 2 Table of Contents Part I: TensorFlow 2 Fundamentals TensorFlow 2 Basics Broadcasting the good and the ugly Take advantage of th

Vahid Kazemi 8.7k Dec 31, 2022
ADSPM: Attribute-Driven Spontaneous Motion in Unpaired Image Translation

ADSPM: Attribute-Driven Spontaneous Motion in Unpaired Image Translation This repository provides a PyTorch implementation of ADSPM. Requirements Pyth

24 Jul 24, 2022
Consistency Regularization for Adversarial Robustness

Consistency Regularization for Adversarial Robustness Official PyTorch implementation of Consistency Regularization for Adversarial Robustness by Jiho

40 Dec 17, 2022
DetCo: Unsupervised Contrastive Learning for Object Detection

DetCo: Unsupervised Contrastive Learning for Object Detection arxiv link News Sparse RCNN+DetCo improves from 45.0 AP to 46.5 AP(+1.5) with 3x+ms trai

Enze Xie 234 Dec 18, 2022
Building a real-time environment using webcam frame division in OpenCV and classify cropped images using a fine-tuned vision transformers on hybryd datasets samples for facial emotion recognition.

Visual Transformer for Facial Emotion Recognition (FER) This project has the aim to build an efficient Visual Transformer for the Facial Emotion Recog

Mario Sessa 8 Dec 12, 2022
Pytorch implementation of PCT: Point Cloud Transformer

PCT: Point Cloud Transformer This is a Pytorch implementation of PCT: Point Cloud Transformer.

Yi_Zhang 265 Dec 22, 2022
Code release for "Self-Tuning for Data-Efficient Deep Learning" (ICML 2021)

Self-Tuning for Data-Efficient Deep Learning This repository contains the implementation code for paper: Self-Tuning for Data-Efficient Deep Learning

THUML @ Tsinghua University 101 Dec 11, 2022