Add object detection pipeline tag, library name, and link to code

README.md

@@ -1,13 +1,21 @@
+---
+license: mit
+library_name: pytorch
+pipeline_tag: object-detection
+---
+
 # TSP3D: Text-guided Sparse Voxel Pruning for Efficient 3D Visual Grounding
 
 This repo contains the official PyTorch implementation for paper [Text-guided Sparse Voxel Pruning for Efficient 3D Visual Grounding](https://arxiv.org/abs/2502.10392).
 
+Code: https://github.com/GWxuan/TSP3D
+
 > Text-guided Sparse Voxel Pruning for Efficient 3D Visual Grounding  
 > [Wenxuan Guo](https://github.com/GWxuan)*, [Xiuwei Xu](https://xuxw98.github.io/)\*, [Ziwei Wang](https://ziweiwangthu.github.io/), [Jianjiang Feng](https://ivg.au.tsinghua.edu.cn/~jfeng/index.html)†, [Jie Zhou](https://scholar.google.com/citations?user=6a79aPwAAAAJ&hl=en&authuser=1), [Jiwen Lu](http://ivg.au.tsinghua.edu.cn/Jiwen_Lu/)
 
 \* Equal contribution † Corresponding author
 
-In this work, we propose an efficient multi-level convolution architecture for <b>3D visual grounding</b>. TSP3D achieves superior performance compared to previous approaches in both <b>inference speed and accuracy</b>.
+In this work, we propose an efficient multi-level convolution architecture for **3D visual grounding**. TSP3D achieves superior performance compared to previous approaches in both **inference speed and accuracy**.
 
 ## Main Results
 + We provide the checkpoints for quick reproduction of the results reported in the paper.