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This repository contains codes for quadtree attention. This repo contains codes for feature matching, image classficiation, object detection and semantic segmentation.

Installation

  1. Compile the quadtree attention operation cd QuadTreeAttention&&python setup.py install
  2. Install the package for each task according to each README.md in the separate directory.

Model Zoo and Baselines

We provide baselines results and model zoo in the following.

Feature matching

News! QuadTree Attention achieves the best single model performance among all public available pretrained models in image matching chanllenge 2022. Please refer to this [post].

  • Quadtree on Feature matching
Method AUC@5 AUC@10 AUC@20 Model
ScanNet 24.9 44.7 61.8 [Google]/[GitHub]
Megadepth 53.5 70.2 82.2 [Google]/[GitHub]

Image classification

  • Quadtree on ImageNet-1K
Method Flops Acc@1 Model
Quadtree-B-b0 0.6 72.0 [Google]/[GitHub]
Quadtree-B-b1 2.3 80.0 [Google]/[GitHub]
Quadtree-B-b2 4.5 82.7 [Google]/[GitHub]
Quadtree-B-b3 7.8 83.8 [Google]/[GitHub]
Quadtree-B-b4 11.5 84.0 [Google]/[GitHub]

Object detection and instance segmentation

  • Quadtree on COCO

Baseline Detectors

Method Backbone Pretrain Lr schd Aug Box AP Mask AP Model
RetinaNet Quadtree-B-b0 ImageNet-1K 1x No 38.4 - [Google]/[GitHub]
RetinaNet Quadtree-B-b1 ImageNet-1K 1x No 42.6 - [Google]/[GitHub]
RetinaNet Quadtree-B-b2 ImageNet-1K 1x No 46.2 - [Google]/[GitHub]
RetinaNet Quadtree-B-b3 ImageNet-1K 1x No 47.3 - [Google]/[GitHub]
RetinaNet Quadtree-B-b4 ImageNet-1K 1x No 47.9 - [Google]/[GitHub]
Mask R-CNN Quadtree-B-b0 ImageNet-1K 1x No 38.8 36.5 [Google]/[GitHub]
Mask R-CNN Quadtree-B-b1 ImageNet-1K 1x No 43.5 40.1 [Google]/[GitHub]
Mask R-CNN Quadtree-B-b2 ImageNet-1K 1x No 46.7 42.4 [Google]/[GitHub]
Mask R-CNN Quadtree-B-b3 ImageNet-1K 1x No 48.3 43.3 [Google]/[GitHub]
Mask R-CNN Quadtree-B-b4 ImageNet-1K 1x No 48.6 43.6 [Google]/[GitHub]

Semantic Segmentation

  • Quadtree on ADE20K
Method Backbone Pretrain Iters mIoU Model
Semantic FPN Quadtree-b0 ImageNet-1K 160K 39.9 [Google]/[GitHub]
Semantic FPN Quadtree-b1 ImageNet-1K 160K 44.7 [Google]/[GitHub]
Semantic FPN Quadtree-b2 ImageNet-1K 160K 48.7 [Google]/[GitHub]
Semantic FPN Quadtree-b3 ImageNet-1K 160K 50.0 [Google]/[GitHub]
Semantic FPN Quadtree-b4 ImageNet-1K 160K 50.6 [Google]/[GitHub]

Citation

@article{tang2022quadtree,
  title={QuadTree Attention for Vision Transformers},
  author={Tang, Shitao and Zhang, Jiahui and Zhu, Siyu and Tan, Ping},
  journal={ICLR},
  year={2022}
}

License

The MIT License (MIT)

Copyright (c) 2022 Shitao Tang

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.