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helonin / RankSeg

[ECCV2022] This is an official implementation of paper "RankSeg: Adaptive Pixel Classification with Image Category Ranking for Segmentation".

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RankSeg: Adaptive Pixel Classification with Image Category Ranking for Segmentation, ECCV 2022

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News

2022.07.19 We rename MLSeg to RankSeg to highlight the importance of our rank-oriented design.

2022.07.04 MLSeg has been accepted by ECCV 2022.

Introduction

The segmentation task has traditionally been formulated as a complete-label pixel classification task to predict a class for each pixel from a fixed number of predefined semantic categories shared by all images or videos. Yet, following this formulation, standard architectures will inevitably encounter various challenges under more realistic settings where the scope of categories scales up (e.g., beyond the level of 1k). On the other hand, in a typical image or video, only a few categories, i.e., a small subset of the complete label are present. Motivated by this intuition, in this paper, we propose to decompose segmentation into two sub-problems: (i) image-level or video-level multi-label classification and (ii) pixel-level rank-adaptive selected-label classification. Given an input image or video, our framework first conducts multi-label classification over the complete label, then sorts the complete label and selects a small subset according to their class confidence scores. We then use a rank-adaptive pixel classifier to perform the pixel-wise classification over only the selected labels, which uses a set of rank-oriented learnable temperature parameters to adjust the pixel classifications scores. Our approach is conceptually general and can be used to improve various existing segmentation frameworks by simply using a lightweight multi-label classification head and rank-adaptive pixel classifier. We demonstrate the effectiveness of our framework with competitive experimental results across four tasks, including image semantic segmentation, image panoptic segmentation, video instance segmentation, and video semantic segmentation. Especially, with our RankSeg, Mask$2$Former gains +0.8%/+0.7%/+0.7% on ADE$20$K panoptic segmentation/YouTubeVIS 2019 video instance segmentation/VSPW video semantic segmentation benchmarks respectively.

  • The RankSeg architecture:

teaser

Image Semantic Segmentation based on DeepLabV3/Segmenter/Swin/BEiT + RankSeg

RankSeg + DeepLabV3

Method Dataset Backbone Crop Size Lr schd mIoU mIoU(ms+flip) config download
DeepLabV3 (Official) COCO-Stuff R101 512x512 20000 37.3 38.4 - -
DeepLabV3 + RankSeg COCO-Stuff R101 512x512 20000 38.4 39.8 - -
DeepLabV3 (Official) ADE20K R101 512x512 80000 44.1 45.2 - -
DeepLabV3 + RankSeg ADE20K R101 512x512 80000 45.5 46.6 - -
DeepLabV3 COCO+LVIS R101 512x512 160000 11.0 - - -
DeepLabV3 + RankSeg COCO+LVIS R101 512x512 160000 12.8 - - -

RankSeg + Segmenter

  • Multi-Scale test is not conducted on ADE20KFull and COCO+LVIS datasets because of memory limits.
Method Dataset Backbone Crop Size Lr schd mIoU mIoU(ms+flip) config download
Segmenter COCO-Stuff ViT-B 512x512 40000 41.9 43.8 - -
Segmenter + RankSeg COCO-Stuff ViT-B 512x512 40000 44.9 46.2 - -
Segmenter COCO-Stuff ViT-B 512x512 80000 43.4 45.2 - -
Segmenter + RankSeg COCO-Stuff ViT-B 512x512 80000 45.7 46.7 - -
Segmenter COCO-Stuff ViT-L 640x640 40000 45.5 47.1 - -
Segmenter + RankSeg COCO-Stuff ViT-B 640x640 40000 46.7 47.9 - -
Segmenter Pascal-Context60 ViT-B 480x480 80000 53.8 54.6 - -
Segmenter + RankSeg Pascal-Context60 ViT-B 480x480 80000 54.7 55.4 - -
Segmenter ADE20K ViT-B 512x512 160000 48.8 50.7 - -
Segmenter + RankSeg ADE20K ViT-B 512x512 160000 49.7 51.4 - -
Segmenter ADE20K ViT-L 640x640 160000 52.0 53.6 - -
Segmenter + RankSeg ADE20K ViT-L 640x640 160000 52.6 54.4 - -
Segmenter ADE20KFull ViT-B 512x512 160000 17.8 - - -
Segmenter + RankSeg ADE20KFull ViT-B 512x512 160000 18.8 - - -
Segmenter COCO+LVIS ViT-B 512x512 320000 19.4 - - -
Segmenter + RankSeg COCO+LVIS ViT-B 512x512 320000 21.3 - - -
Segmenter COCO+LVIS ViT-B 640x640 320000 23.7 - - -
Segmenter + RankSeg COCO+LVIS ViT-B 640x640 320000 24.6 - - -

RankSeg + Swin

Method Dataset Backbone Crop Size Lr schd mIoU mIoU(ms+flip) config download
Swin COCO-Stuff Swin-B 512x512 40000 45.7 47.2 - -
Swin + RankSeg COCO-Stuff Swin-B 512x512 40000 46.6 47.9 - -
Swin (Official) ADE20K Swin-B 512x512 160000 50.8 52.4 - -
Swin + RankSeg ADE20K Swin-B 512x512 160000 51.4 53.0 - -
Swin COCO+LVIS Swin-B 512x512 160000 20.3 - - -
Swin + RankSeg COCO+LVIS Swin-B 512x512 160000 20.8 - - -

RankSeg + BEiT

Method Dataset Backbone Crop Size Lr schd mIoU mIoU(ms+flip) config download
BEiT (Official) ADE20K BEiT-L 640x640 160000 56.7 57.0 - -
RankSeg + BEiT ADE20K BEiT-L 640x640 160000 57.0 57.8 - -
BEiT (Official) COCO-Stuff BEiT-L 640x640 160000 49.7 49.9 - -
RankSeg + BEiT COCO-Stuff BEiT-L 640x640 160000 49.9 50.3 - -

Image Semantic & Panoptic Segmentation based on MaskFormer + RankSeg

Semantic Segmentation

Method Dataset Backbone Crop Size Lr schd mIoU mIoU(ms+flip) config download
MaskFormer ADE20K Swin-B 512x512 160000 52.7 53.9 - -
MaskFormer + RankSeg ADE20K Swin-B 512x512 160000 53.9 55.1 - -

Panoptic Segmentation

Method Dataset Backbone Crop Size Lr schd PQ PQ-th PQ-st RQ RQ-th RQ-st SQ SQ-th SQ-st config download
MaskFormer ADE20K R50 640x640 720000 34.7 32.2 39.7 42.8 40.1 48.1 76.7 76.9 76.3 - -
MaskFormer + RankSeg ADE20K R50 640x640 720000 36.5 34.5 40.6 44.9 42.8 48.9 76.8 77.1 76.0 - -
MaskFormer + RankSeg + GT ADE20K R50 640x640 720000 44.3 39.7 53.5 54.5 49.5 64.6 79.6 78.6 81.7 - -

Image Semantic & Image Panoptic & Video Semantic & Video Instance Segmentation based on Mask2Former + RankSeg

Semantic Segmentation

Method Dataset Backbone Crop Size Lr schd mIoU mIoU(ms+flip) config download
Mask2Former ADE20K Swin-B 512x512 160000 53.9 55.1 - -
Mask2Former + RankSeg ADE20K Swin-B 512x512 160000 54.9 55.6 - -
Mask2Former ADE20K Swin-L 512x512 160000 56.1 57.3 - -
Mask2Former + RankSeg ADE20K Swin-L 512x512 160000 56.5 58.0 - -

Panoptic Segmentation

Method Dataset Backbone Crop Size Lr schd PQ config download
Mask2Former ADE20K Swin-L 512x512 160000 48.1 - -
Mask2Former + RankSeg ADE20K Swin-L 512x512 160000 48.9 - -

Video Semantic Segmentation

Method Dataset Backbone Crop Size Lr schd mIoU config download
Mask2Former VSPW R101 512x512 6000 45.9 - -
Mask2Former + RankSeg VSPW R101 512x512 6000 47.0 - -
Mask2Former VSPW Swin-L 512x512 6000 59.4 - -
Mask2Former + RankSeg VSPW Swin-L 512x512 6000 60.1 - -

Video Instance Segmentation

Method Dataset Backbone Crop Size Lr schd AP config download
Mask2Former YoutubeVIS2019 R101 512x512 6000 49.2 - -
Mask2Former + RankSeg YoutubeVIS2019 R101 512x512 6000 50.5 - -
Mask2Former YoutubeVIS2019 Swin-B 512x512 6000 59.5 - -
Mask2Former + RankSeg YoutubeVIS2019 Swin-B 512x512 6000 60.3 - -
Mask2Former YoutubeVIS2019 Swin-L 512x512 6000 60.4 - -
Mask2Former + RankSeg YoutubeVIS2019 Swin-L 512x512 6000 61.1 - -

Citation

If you find this project useful in your research, please consider cite:

@article{HYYH2022RankSeg,
  title={RankSeg: Adaptive Pixel Classification with Image Category Ranking for Segmentation},
  author={Haodi He and Yuhui Yuan and Xiangyu Yue and Han Hu},
  booktitle={arXiv preprint arXiv:2203.04187},
  year={2022}
}

git diff-index HEAD
git subtree add -P pose <url to sub-repo> <sub-repo branch>

About

[ECCV2022] This is an official implementation of paper "RankSeg: Adaptive Pixel Classification with Image Category Ranking for Segmentation".