Update README.md

README.md

@@ -1,3 +1,28 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+datasets:
+- bitmind/MS-COCO
+- Voxel51/VisDrone2019-DET
+- huggan/cityscapes
+- yizhangdev/pascal-voc
+---
+A<sup>3</sup>-FPN: Asymptotic Content-Aware Pyramid Attention Network for Dense Visual Prediction
+---------------------
+
+This repository is the checkpoints for our paper "[A<sup>3</sup>-FPN: Asymptotic Content-Aware Pyramid Attention Network for Dense Visual Prediction](https://doi.org/10.48550/arXiv.2604.10210)".
+A<sup>3</sup>-FPN employs a horizontally-spread column network that enables asymptotically global feature interaction and disentangles each level from all hierarchical representations. 
+In feature fusion, it collects supplementary content from the adjacent level to generate position-wise offsets and weights for context-aware resampling, and learns deep context reweights to improve intra-category similarity. 
+In feature reassembly, it further strengthens intra-scale discriminative feature learning and reassembles redundant features based on information content and spatial variation of feature maps. 
+Extensive experiments on MS COCO, VisDrone2019-DET and Cityscapes demonstrate that A<sup>3</sup>-FPN can be easily integrated into state-of-the-art CNN and Transformer-based architectures, yielding remarkable performance gains.
+
+If you find A<sup>3</sup>-FPN useful in your research, please consider citing:
+```
+@article{qin2026a3,
+  title={A3-FPN: Asymptotic Content-Aware Pyramid Attention Network for Dense Visual Prediction},
+  author={Qin, Meng'en and Song, Yu and Zhao, Quanling and Yang, Xiaodong and Che, Yingtao and Yang, Xiaohui},
+  journal={arXiv preprint arXiv:2604.10210},
+  year={2026}
+}
+```