add: iccv 2023.

_bibliography/pubs.bib

@@ -3,6 +3,13 @@
 
 @string{aps = {American Physical Society,}}
 
+@inproceedings{zhu2023improving,
+  title={Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning},
+  author={Zhu, Kaijie and Hu, Xixu and Wang, Jindong and Xie, Xing and Yang, Ge},
+  booktitle={International Conference on Computer Vision (ICCV)},
+  year={2023},
+}
+
 @inproceedings{qin2023generalizable,
   title={Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning},
   author={Qin, Xin and Wang, Jindong and Ma, Shuo and Lu, Wang and Zhu, Yongchun and Xie, Xing and Chen, Yiqiang},

_news/iccv23.md

@@ -0,0 +1,7 @@
+---
+layout: post
+date: 2023-07-18
+inline: true
+---
+
+Paper *Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning* is accepted by ICCV 2023!

_pages/others.md

@@ -15,6 +15,8 @@ Current interns:
 Alumni:
 
 - 2023.03 -- 2023.06, Kaijie Zhu, Master @ Institute of Automation, CAS.
+  - Topics: adversarial machine learning and large language language models.
+  - Outcomes during internship: ICCV'23, [Project SearchAnything](https://github.com/Immortalise/SearchAnything).
 - 2022.10 -- 2023.03, [Xixu Hu](https://xixuhu.github.io/), Ph.D @ City University of Hong Kong.
 - 2022.07 -- 2023.03, [Runkai Zheng](https://scholar.google.com/citations?user=52haRQ0AAAAJ&hl=en), Master @ Chinese University of Hong Kong (Shenzhen).
 - 2021.11 -- 2022.10, [Yidong Wang](https://qianlanwyd.github.io/), Master @ Tokyo Institute of Technology. Now: Ph.D in PKU. [[MSRA official blog](https://www.msra.cn/zh-cn/news/outreach-articles/%e5%ae%9e%e4%b9%a0%e6%b4%be%ef%bd%9c%e7%8e%8b%e4%b8%80%e6%a0%8b%ef%bc%9a%e4%b8%bb%e5%8a%a8%e5%b0%b1%e4%bc%9a%e6%9c%89%e6%95%85%e4%ba%8b%ef%bc%81%e9%ab%98%e6%95%88%e7%a7%91%e7%a0%94%e7%a7%98%e8%af%80)]

_pages/research.md

@@ -22,9 +22,13 @@ Our research consists of the following topics with selected publications: [[View
 - **[ACL'23 findings]** [GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective](https://arxiv.org/abs/2211.08073). Linyi Yang, Shuibai Zhang, Libo Qin, Yafu Li, Yidong Wang, Hanmeng Liu, Jindong Wang, Xing Xie, Yue Zhang.
 - **[ICLR'23 large model workshop]** [On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective](https://arxiv.org/abs/2302.12095). Jindong Wang, Xixu Hu, Wenxin Hou, Hao Chen, Runkai Zheng, Yidong Wang, Linyi Yang, Haojun Huang, Wei Ye, Xiubo Geng, Binxin Jiao, Yue Zhang, and Xing Xie.
 - [arXiv'23] [Exploring Vision-Language Models for Imbalanced Learning](https://arxiv.org/abs/2304.01457). Yidong Wang, Zhuohao Yu, Jindong Wang, Qiang Heng, Hao Chen, Wei Ye, Rui Xie, Xing Xie, Shikun Zhang. [[code](https://github.com/Imbalance-VLM/Imbalance-VLM)]
+- Open source:
+  - [Project SearchAnything](https://github.com/Immortalise/SearchAnything): semantic local search.
+
 
 ##### Out-of-distribution (Domain) generalization and adaptation for distribution shift
 
+- **[ICCV'23]** Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning. Kaijie Zhu, Xixu Hu, Jindong Wang, Xing Xie, Ge Yang.
 - **[ICLR'23]** [Out-of-distribution Representation Learning for Time Series Classification](https://arxiv.org/abs/2209.07027). Wang Lu, Jindong Wang, Xinwei Sun, Yiqiang Chen, and Xing Xie.
 - **[KDD'23]** [Domain-Specific Risk Minimization for Out-of-Distribution Generalization](https://arxiv.org/pdf/2208.08661.pdf). YiFan Zhang, Jindong Wang, Jian Liang, Zhang Zhang, Baosheng Yu, Liang Wang, Xing Xie, and Dacheng Tao.
 - **[KDD'23]** [Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning](ddd). Xin Qin, Jindong Wang, Shuo Ma, Wang Lu, Yongchun Zhu, Xing Xie, Yiqiang Chen.