Update README.md

README.md

@@ -1,3 +1,28 @@
----
-license: gpl-3.0
----
+---
+license: gpl-3.0
+---
+
+<div align="center">
+  <h1>Mamba-YOLO-World</h1>
+  <h3>Mamba-YOLO-World: Marrying YOLO-World with Mamba for Open-Vocabulary Detection</h3>
+  Haoxuan Wang, Qingdong He, Jinlong Peng, Hao Yang, Mingmin Chi, Yabiao Wang
+
+  <br>
+  <br>
+
+  [![arxiv paper](https://img.shields.io/badge/arXiv-Paper-red)](https://arxiv.org/abs/2409.08513)
+
+</div>
+
+
+## Abstract
+Open-vocabulary detection (OVD) aims to detect objects beyond a predefined set of categories.
+As a pioneering model incorporating the YOLO series into OVD, YOLO-World is well-suited for scenarios prioritizing speed and efficiency.
+However, its performance is hindered by its neck feature fusion mechanism, which causes the quadratic complexity and the limited guided receptive fields. 
+To address these limitations, we present Mamba-YOLO-World, a novel YOLO-based OVD model employing the proposed MambaFusion Path Aggregation Network (MambaFusion-PAN) as its neck architecture.
+Specifically, we introduce an innovative State Space Model-based feature fusion mechanism consisting of a Parallel-Guided Selective Scan algorithm and a Serial-Guided Selective Scan algorithm with linear complexity and globally guided receptive fields.
+It leverages multi-modal input sequences and mamba hidden states to guide the selective scanning process.
+Experiments demonstrate that our model outperforms the original YOLO-World on the COCO and LVIS benchmarks in both zero-shot and fine-tuning settings while maintaining comparable parameters and FLOPs. 
+Additionally, it surpasses existing state-of-the-art OVD methods with fewer parameters and FLOPs.
+
+For our code and more information, please turn to https://github.com/Xuan-World/Mamba-YOLO-World