ylf add pub: FGVP & FGVTP

Author: ylingfeng

index.html

@@ -960,6 +960,36 @@ <h2 id="experience">News</h2>
         <h2 id="publications">Selected Publications</h2>
 	(* indicates equal contribution, # corresponding author)
 
+	<div class="paper"><img class="paper" src="./resources/paper_icon/TPAMI_2025_FGVTP.png" title="Fine-Grained Visual Text Prompting">
+        <div><strong>Fine-Grained Visual Text Prompting</strong>
+            <br>Lingfeng Yang, Xiang Li#, Yueze Wang, Xinlong Wang, Jian Yang#<br>in TPAMI, 2025<br>
+            <a href="https://ieeexplore.ieee.org/document/10763465">[Paper]</a>
+            <a href="./resources/bibtex/TPAMI_2025_FGVTP.bib">[BibTex]</a>
+            <a href="https://github.com/ylingfeng/FGVP">[Code]</a><img src="https://img.shields.io/github/stars/ylingfeng/FGVP?style=social"/>
+            <br>
+            <alert>
+            FGVTP is an improved fine-grained multimodal prompting method over FGVP, enhancing large multimodal models’ localization and grounding via consistent visual–textual alignment.
+            </alert>
+        </div>
+        <div class="spanner"></div>
+    </div>
+
+	<div class="paper"><img class="paper" src="./resources/paper_icon/NeurIPS_2023_FGVP.png" title="Fine-Grained Visual Prompting">
+        <div><strong>Fine-Grained Visual Prompting</strong>
+            <br>Lingfeng Yang, Yueze Wang, Xiang Li#, Xinlong Wang, Jian Yang#<br>in NeurIPS, 2023<br>
+            <a href="https://proceedings.neurips.cc/paper_files/paper/2023/file/4e9fa6e716940a7cfc60c46e6f702f52-Paper-Conference.pdf">[Paper]</a>
+            <a href="./resources/bibtex/NeurIPS-2023-fine-grained-visual-prompting-Bibtex.bib">[BibTex]</a>
+            <a href="https://github.com/ylingfeng/FGVP">[Code]</a><img src="https://img.shields.io/github/stars/ylingfeng/FGVP?style=social"/>
+            <a href="https://mp.weixin.qq.com/s?search_click_id=10536340093298438394-1705732863737-1260009527&__biz=MzUxMDE4MzAzOA==&mid=2247714099&idx=1&sn=efe4d92ccece149d624d44a19f75404f&chksm=f8982f6663c6f4103967040294490fb7419803ceb6b54f2e79de728104a1858ad03f011d3fb8&scene=7&subscene=90&sessionid=1705732839&clicktime=1705732863&enterid=1705732863&ascene=65&fasttmpl_type=0&fasttmpl_fullversion=7038836-zh_CN-zip&fasttmpl_flag=0&realreporttime=1705732863790&devicetype=android-33&version=28002d3b&nettype=WIFI&abtest_cookie=AAACAA%3D%3D&lang=zh_CN&countrycode=CN&exportkey=n_ChQIAhIQTY3OsEwNdtlJy0RxUEMZyxLcAQIE97dBBAEAAAAAAJ%2F5F8UMLd0AAAAOpnltbLcz9gKNyK89dVj0fDJfc0iQOozTOSv7wroTFtyx6pfMLQW9ACiiUD2XPYTJToJQxVNxvrF5tAIC8R0SbOS35hwJULATy64LUtXxEgmsCoz6Cqv01v%2B25HzaDWybt6vi82M5Lad5HaUdHZAgh4kTKQl9Lri9nQxeptfavWT7F389xOk%2BXh7B4nHuFz%2BeaRdMmZf6lLv3kLpf10%2BJykklCd3SfLyGkE68DPfh1hmFhext2v%2BZTOids%2B0QavnzY7GPOQE%3D&pass_ticket=h3SZ5GzwbdiBvmS547xoTsCldqEAFLvligHaiMY%2BXuAaSiUHNNO2iFTVImHJqOpfAucoZ0LcWe34Hs99pbaVbA%3D%3D&wx_header=3&poc_token=HEYkVWijKQwOws52LqNI8BFkPicAMjsAOeCl7vHt">[中文解读]</a>
+            <a href="https://www.bilibili.com/video/BV1qw411873s/?spm_id_from=333.999.0.0&vd_source=55bfc02adba971ea9a2c7d47e95180cc">[中文视频]</a>
+            <br>
+            <alert>
+            FGVP is a visual prompting technique that improves referring expression comprehension by highlighting regions of interest via fine-grained segmentation, achieving better accuracy with faster inference than state-of-the-art methods.
+            </alert>
+        </div>
+        <div class="spanner"></div>
+    </div>
+
 	<div class="paper"><img class="paper" src="./resources/paper_icon/ICCV_2023_LSKNet.png"
 				   title="Large Selective Kernel Network for Remote Sensing Object Detection">
             <div><strong>Large Selective Kernel Network for Remote Sensing Object Detection</strong><br>

resources/bibtex/NeurIPS-2023-fine-grained-visual-prompting-Bibtex.bib

@@ -0,0 +1,11 @@
+@inproceedings{NEURIPS2023_4e9fa6e7,
+ author = {Yang, Lingfeng and Wang, Yueze and Li, Xiang and Wang, Xinlong and Yang, Jian},
+ booktitle = {Advances in Neural Information Processing Systems},
+ editor = {A. Oh and T. Naumann and A. Globerson and K. Saenko and M. Hardt and S. Levine},
+ pages = {24993--25006},
+ publisher = {Curran Associates, Inc.},
+ title = {Fine-Grained Visual Prompting},
+ url = {https://proceedings.neurips.cc/paper_files/paper/2023/file/4e9fa6e716940a7cfc60c46e6f702f52-Paper-Conference.pdf},
+ volume = {36},
+ year = {2023}
+}

resources/bibtex/TPAMI_2025_FGVTP.bib

@@ -0,0 +1,10 @@
+@ARTICLE{10763465,
+  author={Yang, Lingfeng and Li, Xiang and Wang, Yueze and Wang, Xinlong and Yang, Jian},
+  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
+  title={Fine-Grained Visual Text Prompting}, 
+  year={2025},
+  volume={47},
+  number={3},
+  pages={1594-1609},
+  keywords={Visualization;Semantics;Image segmentation;Crops;Tuning;Detectors;Proposals;Location awareness;Grounding;Gray-scale;Object localization;prompt engineering;referring expression comprehension;vision-language model;zero-shot},
+  doi={10.1109/TPAMI.2024.3504568}}