X-Decoder: Generalized Decoding for Pixel, Image, and Language

[Project Page] [Paper] [Hugging Face Demo] [Video]

by Xueyan Zou*, Zi-Yi Dou*, Jianwei Yang*, Zhe Gan, Linjie Li, Chunyuan Li, Xiyang Dai, Harkirat Behl, Jianfeng Wang, Lu Yuan, Nanyun Peng, Lijuan Wang, Yong Jae Lee^, Jianfeng Gao^.

🌻 Try out our Hugging Face Demo!

🎶 Introduction

X-Decoder is a generalized decoding model that can generate pixel-level segmentation and token-level texts seamlessly!

It achieves:

State-of-the-art results on open-vocabulary segmentation and referring segmentation on eight datasets;
Better or competitive finetuned performance to generalist and specialist models on segmentation and VL tasks;
Friendly for efficient finetuning and flexible for novel task composition.

It supports:

One suite of parameters pretrained for Semantic/Instance/Panoptic Segmentation, Referring Segmentation, Image Captioning, and Image-Text Retrieval;
One model architecture finetuned for Semantic/Instance/Panoptic Segmentation, Referring Segmentation, Image Captioning, Image-Text Retrieval and Visual Question Answering (with an extra cls head);
Zero-shot task composition for Region Retrieval, Referring Captioning, Image Editing.

🔥 Updates

[2022.12.21] We release inference code of X-Decoder.
[2022.12.21] We release Focal-T pretrained checkpoint.
[2022.12.21] We release open-vocabulary segmentation benchmark.

❄️ TODO

Release Training and Prompt Tuning code
Release Finetuned model
Release Base and Large model

Getting Started

Installation

pip3 install torch==1.13.1 torchvision==0.14.1 --extra-index-url https://download.pytorch.org/whl/cu113
python -m pip install 'git+https://github.com/MaureenZOU/detectron2-xyz.git'
pip install git+https://github.com/cocodataset/panopticapi.git
python -m pip install -r requirements.txt

To prepare the dataset: DATASET.md

Open Vocabulary Segmentation

mpirun -n 8 python eval.py evaluate --conf_files configs/xdecoder/svlp_focalt_lang.yaml  --overrides WEIGHT /pth/to/ckpt

Note: Due to zero-padding, filling a single gpu with multiple images may decrease the performance.

Inference Demo

# For Segmentation Tasks
python demo/demo_captioning.py evaluate --conf_files configs/xdecoder/svlp_focalt_lang.yaml  --overrides WEIGHT /pth/to/xdecoder_focalt_best_openseg.pt
# For VL Tasks
python demo/demo_captioning.py evaluate --conf_files configs/xdecoder/svlp_focalt_lang.yaml  --overrides WEIGHT /pth/to/xdecoder_focalt_last_novg.pt

Model Zoo

		ADE			ADE-full	SUN	SCAN		SCAN40	Cityscape			BDD
model	ckpt	PQ	AP	mIoU	mIoU	mIoU	PQ	mIoU	mIoU	PQ	mAP	mIoU	PQ	mIoU
X-Decoder	BestSeg Tiny	19.1	10.1	25.1	6.2	35.7	30.3	38.4	22.4	37.7	18.5	50.2	16.9	47.6

X-Decoder NoVG Tiny
X-Decoder Last Tiny

Additional Results

Finetuned ADE 150 (32 epochs)

Model	Task	Log	PQ	mAP	mIoU
X-Decoder (davit-d5,Deformable)	PanoSeg	log	52.4	38.7	59.1

Acknowledgement

We appreciate the contructive dicussion with Haotian Zhang, and inspiration from GLIP! Also we thank the solid codebase of Mask2Former, and Hugging Face to sponsor GPU for our Demo!

Citation

@article{zou2022xdecoder,
  author      = {Zou, Xueyan and Dou, Zi-Yi and Yang, Jianwei and Gan, Zhe and Li, Linjie and Li, Chunyuan and Dai, Xiyang and Wang, Jianfeng and Yuan, Lu and Peng, Nanyun and Wang, Lijuan and Lee, Yong Jae and Gao, Jianfeng},
  title       = {Generalized Decoding for Pixel, Image and Language},
  publisher   = {arXiv},
  year        = {2022},
}

hzhang57 / X-Decoder