ReferFormer - Official Implementation of ReferFormer

Overview

License Framework

PWC PWC

The official implementation of the paper:

Language as Queries for Referring
Video Object Segmentation

Language as Queries for Referring Video Object Segmentation

Jiannan Wu, Yi Jiang, Peize Sun, Zehuan Yuan, Ping Luo

Abstract

In this work, we propose a simple and unified framework built upon Transformer, termed ReferFormer. It views the language as queries and directly attends to the most relevant regions in the video frames. Concretely, we introduce a small set of object queries conditioned on the language as the input to the Transformer. In this manner, all the queries are obligated to find the referred objects only. They are eventually transformed into dynamic kernels which capture the crucial object-level information, and play the role of convolution filters to generate the segmentation masks from feature maps. The object tracking is achieved naturally by linking the corresponding queries across frames. This mechanism greatly simplifies the pipeline and the end-to-end framework is significantly different from the previous methods. Extensive experiments on Ref-Youtube-VOS, Ref-DAVIS17, A2D-Sentences and JHMDB-Sentences show the effectiveness of ReferFormer.

Requirements

We test the codes in the following environments, other versions may also be compatible:

  • CUDA 11.1
  • Python 3.7
  • Pytorch 1.8.1

Installation

Please refer to install.md for installation.

Data Preparation

Please refer to data.md for data preparation.

We provide the pretrained model for different visual backbones. You may download them here and put them in the directory pretrained_weights.

After the organization, we expect the directory struture to be the following:

ReferFormer/
├── data/
│   ├── ref-youtube-vos/
│   ├── ref-davis/
│   ├── a2d_sentences/
│   ├── jhmdb_sentences/
├── davis2017/
├── datasets/
├── models/
├── scipts/
├── tools/
├── util/
├── pretrained_weights/
├── eval_davis.py
├── main.py
├── engine.py
├── inference_ytvos.py
├── inference_davis.py
├── opts.py
...

Model Zoo

All the models are trained using 8 NVIDIA Tesla V100 GPU. You may change the --backbone parameter to use different backbones (see here).

Note: If you encounter the OOM error, please add the command --use_checkpoint (we add this command for Swin-L, Video-Swin-S and Video-Swin-B models).

Ref-Youtube-VOS

To evaluate the results, please upload the zip file to the competition server.

Backbone J&F CFBI J&F Pretrain Model Submission CFBI Submission
ResNet-50 55.6 59.4 weight model link link
ResNet-101 57.3 60.3 weight model link link
Swin-T 58.7 61.2 weight model link link
Swin-L 62.4 63.3 weight model link link
Video-Swin-T* 55.8 - - model link -
Video-Swin-T 59.4 - weight model link -
Video-Swin-S 60.1 - weight model link -
Video-Swin-B 62.9 - weight model link -

* indicates the model is trained from scratch.

Ref-DAVIS17

As described in the paper, we report the results using the model trained on Ref-Youtube-VOS without finetune.

Backbone J&F J F Model
ResNet-50 58.5 55.8 61.3 model
Swin-L 60.5 57.6 63.4 model
Video-Swin-B 61.1 58.1 64.1 model

A2D-Sentences

The pretrained models are the same as those provided for Ref-Youtube-VOS.

Backbone Overall IoU Mean IoU mAP Pretrain Model
Video-Swin-T 77.6 69.6 52.8 weight model | log
Video-Swin-S 77.7 69.8 53.9 weight model | log
Video-Swin-B 78.6 70.3 55.0 weight model | log

JHMDB-Sentences

As described in the paper, we report the results using the model trained on A2D-Sentences without finetune.

Backbone Overall IoU Mean IoU mAP Model
Video-Swin-T 71.9 71.0 42.2 model
Video-Swin-S 72.8 71.5 42.4 model
Video-Swin-B 73.0 71.8 43.7 model

Get Started

Please see Ref-Youtube-VOS, Ref-DAVIS17, A2D-Sentences and JHMDB-Sentences for details.

Acknowledgement

This repo is based on Deformable DETR and VisTR. We also refer to the repositories MDETR and MTTR. Thanks for their wonderful works.

Citation

@article{wu2022referformer,
      title={Language as Queries for Referring Video Object Segmentation}, 
      author={Jiannan Wu and Yi Jiang and Peize Sun and Zehuan Yuan and Ping Luo},
      journal={arXiv preprint arXiv:2201.00487},
      year={2022},
}
Owner
Jonas Wu
The University of Hong Kong. PhD Candidate. Computer Vision.
Jonas Wu
Implementation of CaiT models in TensorFlow and ImageNet-1k checkpoints. Includes code for inference and fine-tuning.

CaiT-TF (Going deeper with Image Transformers) This repository provides TensorFlow / Keras implementations of different CaiT [1] variants from Touvron

Sayak Paul 9 Jun 26, 2022
Official implementation of "MetaSDF: Meta-learning Signed Distance Functions"

MetaSDF: Meta-learning Signed Distance Functions Project Page | Paper | Data Vincent Sitzmann*, Eric Ryan Chan*, Richard Tucker, Noah Snavely Gordon W

Vincent Sitzmann 100 Jan 01, 2023
Resilient projection-based consensus actor-critic (RPBCAC) algorithm

Resilient projection-based consensus actor-critic (RPBCAC) algorithm We implement the RPBCAC algorithm with nonlinear approximation from [1] and focus

Martin Figura 5 Jul 12, 2022
This is the repo for our work "Towards Persona-Based Empathetic Conversational Models" (EMNLP 2020)

Towards Persona-Based Empathetic Conversational Models (PEC) This is the repo for our work "Towards Persona-Based Empathetic Conversational Models" (E

Zhong Peixiang 35 Nov 17, 2022
GUI for a Vocal Remover that uses Deep Neural Networks.

GUI for a Vocal Remover that uses Deep Neural Networks.

4.4k Jan 07, 2023
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
MT3: Multi-Task Multitrack Music Transcription

MT3: Multi-Task Multitrack Music Transcription MT3 is a multi-instrument automatic music transcription model that uses the T5X framework. This is not

Magenta 867 Dec 29, 2022
InterFaceGAN - Interpreting the Latent Space of GANs for Semantic Face Editing

InterFaceGAN - Interpreting the Latent Space of GANs for Semantic Face Editing Figure: High-quality facial attributes editing results with InterFaceGA

GenForce: May Generative Force Be with You 1.3k Jan 09, 2023
FID calculation with proper image resizing and quantization steps

clean-fid: Fixing Inconsistencies in FID Project | Paper The FID calculation involves many steps that can produce inconsistencies in the final metric.

Gaurav Parmar 606 Jan 06, 2023
Relaxed-machines - explorations in neuro-symbolic differentiable interpreters

Relaxed Machines Explorations in neuro-symbolic differentiable interpreters. Baby steps: inc_stop Libraries JAX Haiku Optax Resources Chapter 3 (∂4: A

Nada Amin 6 Feb 02, 2022
Apply our monocular depth boosting to your own network!

MergeNet - Boost Your Own Depth Boost custom or edited monocular depth maps using MergeNet Input Original result After manual editing of base You can

Computational Photography Lab @ SFU 142 Dec 17, 2022
Pytorch implementation of TailCalibX : Feature Generation for Long-tail Classification

TailCalibX : Feature Generation for Long-tail Classification by Rahul Vigneswaran, Marc T. Law, Vineeth N. Balasubramanian, Makarand Tapaswi [arXiv] [

Rahul Vigneswaran 34 Jan 02, 2023
Computer Vision application in the web

Computer Vision application in the web Preview Usage Clone this repo git clone https://github.com/amineHY/WebApp-Computer-Vision-streamlit.git cd Web

Amine Hadj-Youcef. PhD 35 Dec 06, 2022
PyTorch implementation of DARDet: A Dense Anchor-free Rotated Object Detector in Aerial Images

DARDet PyTorch implementation of "DARDet: A Dense Anchor-free Rotated Object Detector in Aerial Images", [pdf]. Highlights: 1. We develop a new dense

41 Oct 23, 2022
A Demo server serving Bert through ONNX with GPU written in Rust with <3

Demo BERT ONNX server written in rust This demo showcase the use of onnxruntime-rs on BERT with a GPU on CUDA 11 served by actix-web and tokenized wit

Xavier Tao 28 Jan 01, 2023
Model Serving Made Easy

The easiest way to build Machine Learning APIs BentoML makes moving trained ML models to production easy: Package models trained with any ML framework

BentoML 4.4k Jan 08, 2023
Improved Fitness Optimization Landscapes for Sequence Design

ReLSO Improved Fitness Optimization Landscapes for Sequence Design Description Citation How to run Training models Original data source Description In

Krishnaswamy Lab 44 Dec 20, 2022
Implementation of momentum^2 teacher

Momentum^2 Teacher: Momentum Teacher with Momentum Statistics for Self-Supervised Learning Requirements All experiments are done with python3.6, torch

jemmy li 121 Sep 26, 2022
(NeurIPS 2021) Realistic Evaluation of Transductive Few-Shot Learning

Realistic evaluation of transductive few-shot learning Introduction This repo contains the code for our NeurIPS 2021 submitted paper "Realistic evalua

Olivier Veilleux 14 Dec 13, 2022
Open-Domain Question-Answering for COVID-19 and Other Emergent Domains

Open-Domain Question-Answering for COVID-19 and Other Emergent Domains This repository contains the source code for an end-to-end open-domain question

7 Sep 27, 2022