This repository contains codes for quadtree attention. This repo contains codes for feature matching, image classficiation, object detection and semantic segmentation.
- 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.
We provide baselines results and model zoo in the following.
- 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] |
- 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] |
- Quadtree on COCO
| 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] |
- 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] |
@article{tang2022quadtree,
title={QuadTree Attention for Vision Transformers},
author={Tang, Shitao and Zhang, Jiahui and Zhu, Siyu and Tan, Ping},
journal={ICLR},
year={2022}
}
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