update from lyx

Author: zcablii

index.html

@@ -433,7 +433,7 @@ <h3 style="margin-top: 10px; margin-bottom: 5px;">赵鹏海</h3>
                 <h3 style="margin-top: 10px; margin-bottom: 5px;">李宇轩</h3>
                 <p><strong>状态:</strong> 博士 </p>
                 <p><strong>研究方向:</strong> <span style="color: #1772d0;">遥感感知、大模型</span> </p>
-                <p><strong>成果:</strong> 2022第二届计图人工智能挑战赛冠军，2022第五届开源创新大赛团体一等奖，2022首届粤港澳大湾区国际算法算例大赛二等奖。在NeurIPS，IJCV，ICCV等顶级会议期刊上发表论文数篇，Google Scholar引用900+。2024 PRCV竞赛和2025 第七届全球校园人工智能算法精英大赛出题人。 </p>
+                <p><strong>成果:</strong> 2022第二届计图人工智能挑战赛冠军，2022第五届开源创新大赛团体一等奖，2022首届粤港澳大湾区国际算法算例大赛二等奖。在NeurIPS，IJCV，ICCV等顶级会议期刊上发表论文数篇，Google Scholar引用1300+。2024 PRCV竞赛和2025 第七届全球校园人工智能算法精英大赛出题人。 </p>
             </div>
             <div style="width: 30%; margin-bottom: 20px; text-align: center;">
                 <img src="./resources/student/陈震元.jpg" style="width: 180px; height: 180px; object-fit: cover; border-radius: 5px; box-shadow: 0 2px 5px rgba(0,0,0,0.2);">
@@ -990,6 +990,42 @@ <h2 id="publications">Selected Publications</h2>
         <div class="spanner"></div>
     </div>
 
+	<div class="paper"><img class="paper" src="./resources/paper_icon/NeurIPS_2024_SARDet.png"
+				   title="Sardet-100k: Towards open-source benchmark and toolkit for large-scale sar object detection">
+            <div><strong>Sardet-100k: Towards open-source benchmark and toolkit for large-scale sar object detection</strong><br>
+		Yuxuan Li, Xiang Li#, Weijie Li, Qibin Hou, Li Liu, Ming-Ming Cheng, Jian Yang#, <br>
+                in NeurIPS, 2024 (Spotlight🎈) <br>
+                <a href="https://proceedings.neurips.cc/paper_files/paper/2024/file/e7eb8128eb26eafbe901348df1dbacdc-Paper-Conference.pdf">[Paper]</a>
+                <a href="./resources/bibtex/NeurIPS_2024_SARDet.txt">[BibTex]</a>
+                <a href="https://github.com/zcablii/SARDet_100K">[Code]</a><img
+                        src="https://img.shields.io/github/stars/zcablii/SARDet_100K?style=social"/>
+                <a href="https://zhuanlan.zhihu.com/p/686785188">[中文解读]</a>
+                <br>
+                <alert> 
+        SARDet-100K is the first COCO-level large-scale multi-class SAR object detection dataset ever created. A SAR object detection pretrain method: Multi-Stage with Filter Augmentation (MSFA) is proposed to tackle the domain gap problems from the perspective of data input, domain transition, and model migration.
+		</alert>
+            </div>
+            <div class="spanner"></div>
+    </div>
+
+	<div class="paper"><img class="paper" src="./resources/paper_icon/ICCV_2023_LSKNet.png"
+				   title="LSKNet: A Foundation Lightweight Backbone for Remote Sensing">
+            <div><strong>LSKNet: A Foundation Lightweight Backbone for Remote Sensing</strong><br>
+		Yuxuan Li, Xiang Li#,  Yimian Dai, Qibin Hou, Li Liu, Yongxiang Liu, Ming-Ming Cheng, Jian Yang#, <br>
+                in IJCV, 2024<br>
+                <a href="http://openaccess.thecvf.com/content/ICCV2023/papers/Li_Large_Selective_Kernel_Network_for_Remote_Sensing_Object_Detection_ICCV_2023_paper.pdf">[Paper]</a>
+                <a href="./resources/bibtex/IJCV_2024_LSKNet.txt">[BibTex]</a>
+                <a href="https://github.com/zcablii/LSKNet">[Code]</a><img
+                        src="https://img.shields.io/github/stars/zcablii/LSKNet?style=social"/>
+                <a href="https://zhuanlan.zhihu.com/p/614449075">[中文解读]</a>
+                <br>
+                <alert> 
+        LSKNet can dynamically adjust its large spatial receptive field to better model the ranging context of various categories of objects in remote sensing scenarios. The lightweight LSKNet backbone network sets new state-of-the-art scores on standard remote sensing classification, object detection, semantic segmentation and change detection benchmarks.
+		</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>
@@ -1000,17 +1036,12 @@ <h2 id="publications">Selected Publications</h2>
                 <a href="https://github.com/zcablii/LSKNet">[Code]</a><img
                         src="https://img.shields.io/github/stars/zcablii/LSKNet?style=social"/>
                 <br>
-                <a href="https://paperswithcode.com/sota/object-detection-in-aerial-images-on-dota-1?p=large-selective-kernel-network-for-remote"><img
-                src="https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/large-selective-kernel-network-for-remote/object-detection-in-aerial-images-on-dota-1"/></a>
-                <a href="https://paperswithcode.com/sota/object-detection-in-aerial-images-on-hrsc2016?p=large-selective-kernel-network-for-remote"><img
-                src="https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/large-selective-kernel-network-for-remote/object-detection-in-aerial-images-on-hrsc2016"/></a>
-                <br>
                 <alert>
-        LSKNet can dynamically adjust its large spatial receptive field to better model the ranging context of various categories of objects in remote sensing scenarios
+        LSKNet can dynamically adjust its large spatial receptive field to better model the ranging context of various categories of objects in remote sensing scenarios.
 		</alert>
             </div>
             <div class="spanner"></div>
-        </div>
+    </div>
 
 	<div class="paper"><img class="paper" src="./resources/paper_icon/AAAI_2023_CTKD.png"
 	     title="Curriculum Temperature for Knowledge Distillation">

resources/bibtex/IJCV_2024_LSKNet.txt

@@ -0,0 +1,8 @@
+@article{Li_2024_IJCV,
+  title={LSKNet: A Foundation Lightweight Backbone for Remote Sensing},
+  author={Li, Yuxuan and Li, Xiang and Dai, Yimain and Hou, Qibin and Liu, Li and Liu, Yongxiang and Cheng, Ming-Ming and Yang, Jian},
+  journal={International Journal of Computer Vision},
+  year={2024},
+  doi = {https://doi.org/10.1007/s11263-024-02247-9},
+  publisher={Springer}
+}

resources/bibtex/NeurIPS_2024_SARDet.txt

@@ -0,0 +1,6 @@
+@inproceedings{li2024sardet100k,
+	title={SARDet-100K: Towards Open-Source Benchmark and ToolKit for Large-Scale SAR Object Detection}, 
+	author={Yuxuan Li and Xiang Li and Weijie Li and Qibin Hou and Li Liu and Ming-Ming Cheng and Jian Yang},
+	year={2024},
+	booktitle={Advances in Neural Information Processing Systems},
+}