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

Installation

Compile the quadtree attention operation cd QuadTreeAttention&&python setup.py install
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)

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.

Tangshitao/QuadTreeAttention