Update Focus-DETR Paper Info (#286)

* update Focus-DETR

* update Focus-DETR

projects/README.md

@@ -18,4 +18,4 @@ Here are projects that are built on detrex which show you use detrex as a librar
 - [Align-DETR: Improving DETR with Simple IoU-aware BCE loss (ArXiv'2023)](./align_detr/)
 - [EVA-01: Exploring the Limits of Masked Visual Representation Learning at Scale (CVPR'2023 Highlight)](./dino_eva/)
 - [EVA-02: A Visual Representation for Neon Genesis (ArXiv'2023)](./dino_eva/)
-- [Less is More: Focus Attention for Efficient DETR](./focus_detr/)
+- [Less is More: Focus Attention for Efficient DETR (ICCV'2023)](./focus_detr/)

projects/focus_detr/README.md

@@ -3,21 +3,33 @@ This is the official implementation of the ICCV 2023 paper "Less is More: Focus
 
 Authors: Dehua Zheng, Wenhui Dong, Hailin Hu, Xinghao Chen, Yunhe Wang.
 
-[[`arXiv (coming soon)`]()] [[`Official Implementation`](https://github.com/huawei-noah/noah-research/tree/master/Focus-DETR)]
+[[`arXiv`](https://arxiv.org/abs/2307.12612)] [[`Official Implementation`](https://github.com/linxid/Focus-DETR)] [[`BibTeX`](#citing-focus-detr)]
+
+
+Focus-DETR is a model that focuses attention on more informative tokens for a better trade-off between computation efficiency and model accuracy. Compared with the state-of-the-art sparse transformed-based detector under the same setting, our Focus-DETR gets comparable complexity while achieving 50.4AP (+2.2) on COCO.
+
+
+## Model Architecture
+
+Our Focus-DETR comprises a backbone network, a Transformer encoder, and a Transformer decoder. We design a foreground token selector (FTS) based on top-down score modulations across multi-scale features. And the selected tokens by a multi-category score predictor and foreground tokens go through the Pyramid Encoder to remedy the limitation of deformable attention in distant information mixing.
+
 
 <div align="center">
   <img src="https://github.com/huawei-noah/noah-research/raw/master/Focus-DETR/assets/model_arch.png"/>
 </div><br/>
 
+
 ## Table of Contents
 - [Focus-DETR](#focus-detr)
+- [Model Architecture](#model-architecture)
 - [Table of Contents](#table-of-contents)
 - [Main Results with Pretrained Models](#main-results-with-pretrained-models)
     - [Pretrained focus\_detr with ResNet Backbone](#pretrained-focus_detr-with-resnet-backbone)
     - [Pretrained focus\_detr with Swin-Transformer Backbone](#pretrained-focus_detr-with-swin-transformer-backbone)
 - [Installation](#installation)
 - [Training](#training)
 - [Evaluation](#evaluation)
+- [Citation](#citing-focus-detr)
 
 ## Main Results with Pretrained Models
 
@@ -180,3 +192,37 @@ cd detrex
 python tools/train_net.py --config-file projects/focus_detr/configs/path/to/config.py --eval-only train.init_checkpoint=/path/to/model_checkpoint
 ```
 - Note that you should download the pretrained model from [Pretrained Weights](#main-results-with-pretrained-models) and `unzip` it to the specific folder then update the `train.init_checkpoint` to the path of pretrained weights.
+
+
+### Result
+
+```bash
+Results of Focus-DETR with Resnet50 backbone:
+IoU metric: bbox
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.479
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.659
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.521
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.323
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.505
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.619
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.372
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.640
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.720
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.568
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.757
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.878
+```
+
+## Citing Focus-DETR
+If you find our work helpful for your research, please consider citing the following BibTeX entry.
+
+```BibTex
+@misc{zheng2023more,
+      title={Less is More: Focus Attention for Efficient DETR}, 
+      author={Dehua Zheng and Wenhui Dong and Hailin Hu and Xinghao Chen and Yunhe Wang},
+      year={2023},
+      eprint={2307.12612},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```