Awesome

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

[Project Page] [Paper] [HuggingFace All-in-One Demo] [HuggingFace Instruct 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^ in CVPR 2023.

:hot_pepper: Getting Started

:point_right: [New] Latest Checkpoints and Numbers:

Note the number in Table 1 in main paper is after task specific finetuning.

:point_right: [New] Installation, Training, Evaluation, Dataset, and Demo Guide

• DATASET.md
• INSTALL.md
• TRAIN.md
• EVALUATION.md
• DEMO.md

:fire: News

• [2023.07.19] :roller_coaster: We are excited to release the x-decoder training code (INSTALL.md, DATASET.md, TRAIN.md, EVALUATION.md)!
• [2023.07.10] We release Semantic-SAM, a universal image segmentation model to enable segment and recognize anything at any desired granularity. Code and checkpoint are available!
• [2023.04.14] We are releasing SEEM, a new universal interactive interface for image segmentation! You can use it for any segmentation tasks, way beyond what X-Decoder can do!

• [2023.03.20] As an aspiration of our X-Decoder, we developed OpenSeeD ([Paper][Code]) to enable open-vocabulary segmentation and detection with a single model, Check it out!
• [2023.03.14] We release X-GPT which is an conversational version of our X-Decoder through GPT-3 langchain!
• [2023.03.01] The Segmentation in the Wild Challenge had been launched and ready for submitting results!
• [2023.02.28] We released the SGinW benchmark for our challenge. Welcome to build your own models on the benchmark!
• [2023.02.27] Our X-Decoder has been accepted by CVPR 2023!
• [2023.02.07] We combine <ins>X-Decoder</ins> (strong image understanding), <ins>GPT-3</ins> (strong language understanding) and <ins>Stable Diffusion</ins> (strong image generation) to make an instructional image editing demo, check it out!
• [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.

:paintbrush: DEMO

:blueberries: [X-GPT]   :strawberry:[Instruct X-Decoder]

:notes: 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.

Acknowledgement

• We appreciate the contructive dicussion with Haotian Zhang
• We build our work on top of Mask2Former
• We build our demos on HuggingFace :hugs: with sponsored GPUs
• We appreciate the discussion with Xiaoyu Xiang during rebuttal

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},
}