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
Exploring Visual Engagement Signals for Representation Learning

Exploring Visual Engagement Signals for Representation Learning Menglin Jia, Zuxuan Wu, Austin Reiter, Claire Cardie, Serge Belongie and Ser-Nam Lim C

Menglin Jia 9 Jul 23, 2022
Official repository for Few-shot Image Generation via Cross-domain Correspondence (CVPR '21)

Few-shot Image Generation via Cross-domain Correspondence Utkarsh Ojha, Yijun Li, Jingwan Lu, Alexei A. Efros, Yong Jae Lee, Eli Shechtman, Richard Zh

Utkarsh Ojha 251 Dec 11, 2022
This repo is about to create the Streamlit application for given ML model.

HR-Attritiion-using-Streamlit This repo is about to create the Streamlit application for given ML model. Problem Statement: Managing peoples at workpl

Pavan Giri 0 Dec 10, 2021
[ICCV 2021] Target Adaptive Context Aggregation for Video Scene Graph Generation

Target Adaptive Context Aggregation for Video Scene Graph Generation This is a PyTorch implementation for Target Adaptive Context Aggregation for Vide

Multimedia Computing Group, Nanjing University 44 Dec 14, 2022
Official code for the CVPR 2022 (oral) paper "Extracting Triangular 3D Models, Materials, and Lighting From Images".

nvdiffrec Joint optimization of topology, materials and lighting from multi-view image observations as described in the paper Extracting Triangular 3D

NVIDIA Research Projects 1.4k Jan 01, 2023
The Official Implementation of Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose [NIPS 2021].

Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose Release Notes The offical PyTorch implementation of Neural View Sy

Angtian Wang 20 Oct 09, 2022
Black-Box-Tuning - Black-Box Tuning for Language-Model-as-a-Service

Black-Box-Tuning Source code for paper "Black-Box Tuning for Language-Model-as-a

Tianxiang Sun 149 Jan 04, 2023
An open-source Deep Learning Engine for Healthcare that aims to treat & prevent major diseases

AlphaCare Background AlphaCare is a work-in-progress, open-source Deep Learning Engine for Healthcare that aims to treat and prevent major diseases. T

Siraj Raval 44 Nov 05, 2022
PerfFuzz: Automatically Generate Pathological Inputs for C/C++ programs

PerfFuzz Performance problems in software can arise unexpectedly when programs are provided with inputs that exhibit pathological behavior. But how ca

Caroline Lemieux 125 Nov 18, 2022
Accepted at ICCV-2021: Workshop on Computer Vision for Automated Medical Diagnosis (CVAMD)

Is it Time to Replace CNNs with Transformers for Medical Images? Accepted at ICCV-2021: Workshop on Computer Vision for Automated Medical Diagnosis (C

Christos Matsoukas 80 Dec 27, 2022
WiFi-based Multi-task Sensing

WiFi-based Multi-task Sensing Introduction WiFi-based sensing has aroused immense attention as numerous studies have made significant advances over re

zhangx289 6 Nov 24, 2022
Orange Chicken: Data-driven Model Generalizability in Crosslinguistic Low-resource Morphological Segmentation

Orange Chicken: Data-driven Model Generalizability in Crosslinguistic Low-resource Morphological Segmentation This repository contains code and data f

Zoey Liu 0 Jan 07, 2022
bio_inspired_min_nets_improve_the_performance_and_robustness_of_deep_networks

Code Submission for: Bio-inspired Min-Nets Improve the Performance and Robustness of Deep Networks Run with docker To build a docker environment, chan

0 Dec 09, 2021
Twin-deep neural network for semi-supervised learning of materials properties

Deep Semi-Supervised Teacher-Student Material Synthesizability Prediction Citation: Semi-supervised teacher-student deep neural network for materials

MLEG 3 Dec 14, 2022
PyTorch implementation of Hierarchical Multi-label Text Classification: An Attention-based Recurrent Network

hierarchical-multi-label-text-classification-pytorch Hierarchical Multi-label Text Classification: An Attention-based Recurrent Network Approach This

Mingu Kang 17 Dec 13, 2022
Source code for paper "Deep Diffusion Models for Robust Channel Estimation", TBA.

diffusion-channels Source code for paper "Deep Diffusion Models for Robust Channel Estimation". Generic flow: Use 'matlab/main.mat' to generate traini

The University of Texas Computational Sensing and Imaging Lab 15 Dec 22, 2022
[AAAI2021] The source code for our paper 《Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion》.

DSM The source code for paper Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion Project Website; Datasets li

Jinpeng Wang 114 Oct 16, 2022
EfficientNetV2-with-TPU - Cifar-10 case study

EfficientNetV2-with-TPU EfficientNet EfficientNetV2 adalah jenis jaringan saraf convolutional yang memiliki kecepatan pelatihan lebih cepat dan efisie

Sultan syach 1 Dec 28, 2021
Code for ACL 2019 Paper: "COMET: Commonsense Transformers for Automatic Knowledge Graph Construction"

To run a generation experiment (either conceptnet or atomic), follow these instructions: First Steps First clone, the repo: git clone https://github.c

Antoine Bosselut 575 Jan 01, 2023
Official implementation of NeurIPS 2021 paper "Contextual Similarity Aggregation with Self-attention for Visual Re-ranking"

CSA: Contextual Similarity Aggregation with Self-attention for Visual Re-ranking PyTorch training code for CSA (Contextual Similarity Aggregation). We

Hui Wu 19 Oct 21, 2022