diff --git a/README.md b/README.md new file mode 100644 index 0000000..07e9d18 --- /dev/null +++ b/README.md @@ -0,0 +1 @@ +This is the source code of Xin-Ni Jiang (姜欣妮)'s public academic website: https://JiangXinni.github.io \ No newline at end of file diff --git a/bibs/D2A2.html b/bibs/D2A2.html new file mode 100644 index 0000000..827a31c --- /dev/null +++ b/bibs/D2A2.html @@ -0,0 +1,20 @@ + + + + Xin-Ni Jiang + + + + + + + + + + @article{jiang2024the,
+   title={The Devil is in the Details: Boosting Guided Depth Super-Resolution via Rethinking Cross-Modal Alignment and Aggregation},
+   author={Jiang, Xinni and Kuang, Zengsheng and Guo, Chunle and Zhang, Ruixun and Lei Cai and Xiao Fan and Li, Chongyi},
+   journal={arXiv preprint arXiv:2401.08123},
+   year={2024}
+ } + \ No newline at end of file diff --git a/images/D2A2/model.png b/images/D2A2/model.png new file mode 100644 index 0000000..fcda606 Binary files /dev/null and b/images/D2A2/model.png differ diff --git a/images/icons/award.png b/images/icons/award.png new file mode 100644 index 0000000..4be2640 Binary files /dev/null and b/images/icons/award.png differ diff --git a/images/icons/email.png b/images/icons/email.png new file mode 100644 index 0000000..4f58444 Binary files /dev/null and b/images/icons/email.png differ diff --git a/images/icons/github.png b/images/icons/github.png new file mode 100644 index 0000000..d2c5907 Binary files /dev/null and b/images/icons/github.png differ diff --git a/images/icons/google.png b/images/icons/google.png new file mode 100644 index 0000000..e117754 Binary files /dev/null and b/images/icons/google.png differ diff --git a/images/icons/line1.gif b/images/icons/line1.gif new file mode 100644 index 0000000..26f656c Binary files /dev/null and b/images/icons/line1.gif differ diff --git a/images/icons/publication.png b/images/icons/publication.png new file mode 100644 index 0000000..74f8254 Binary files /dev/null and b/images/icons/publication.png differ diff --git a/images/icons/sun.png b/images/icons/sun.png new file mode 100644 index 0000000..dfbab28 Binary files /dev/null and b/images/icons/sun.png differ diff --git a/images/icons/zhihu.png b/images/icons/zhihu.png new file mode 100644 index 0000000..525c9b8 Binary files /dev/null and b/images/icons/zhihu.png differ diff --git a/images/logo.png b/images/logo.png new file mode 100644 index 0000000..85d090d Binary files /dev/null and b/images/logo.png differ diff --git a/images/me.jpg b/images/me.jpg new file mode 100644 index 0000000..2b72053 Binary files /dev/null and b/images/me.jpg differ diff --git a/index.html b/index.html new file mode 100644 index 0000000..d10f6cb --- /dev/null +++ b/index.html @@ -0,0 +1,166 @@ + + + + Xin-Ni Jiang + + + + + + + + + + + + + +
+ + + + + + +
+

+ + Xin-Ni Jiang (姜欣妮) +

+

+ + + Github  |  + + Email: xinnijiang@mail.nankai.edu.cn + +

+

+ Xin-Ni Jiang is a master student at Nankai University, + majoring in computer science, + under the supervision of Prof. Xiu-Li Shao and Chun-Le Guo. + She received the B.E. degree from Nankai University (NKU) in 2022. + Her research interests are in computer vision and machine learning, + with a particular focus on image enhancement and restoration. +

+ 		+ profile photo +
+ + + + + + Publications + + +

+ + + + + + + + + +
+ + + + + The Devil is in the Details: Boosting Guided Depth Super-Resolution via Rethinking Cross-Modal Alignment and Aggregation + +

+
+ Xin-Ni Jiang#, Zeng-Sheng Kuang#, Chun-Le Guo*, Rui-Xun Zhang, Lei Cai, Xiao Fan, Chong-Yi Li* +
+ + + + +
+

+ PDF  |  + + Project  |  + Code  |  + BibTex +

+
+ + + +
+ + + + diff --git a/projects/D2A2/index.html b/projects/D2A2/index.html new file mode 100644 index 0000000..7f42872 --- /dev/null +++ b/projects/D2A2/index.html @@ -0,0 +1,317 @@ + + + + The Devil is in the Details: Boosting Guided Depth Super-Resolution via Rethinking Cross-Modal Alignment and Aggregation + + + + + + + + + + +
+ +
+ The Devil is in the Details: Boosting Guided Depth Super-Resolution via Rethinking Cross-Modal Alignment and Aggregation +
+
+ Xin-Ni Jiang1,#    + Zeng-Sheng Kuang2,#    + Chun-Le Guo1,*    + Rui-Xun Zhang3    + Lei Cai4    + Xiao Fan5    + Chong-Yi Li1,*    +
+
+ 1Nankai University    + 2South China University of Technology    + 3Peking University   
+ 4Henan Institute of Science and Technology    + 5MicroBT Electronics Technology Co., Ltd    +
+ + +
+ + + +
+
+

🔥 Powerful Super-Resolution Reconstruction Ability 🔥

+

+ Our D2A2 achieves amazing super-resolution performance benefits from Dynamic Dual Alignment and Mask-to-Pixel Feature Aggregation. + Here are some typical super-resolution cases. +

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+

Abstract

+

+ Guided depth super-resolution (GDSR) involves restoring missing depth details using the high-resolution RGB image of the same scene. Previous approaches have struggled with the heterogeneity and complementarity of the multi-modal inputs, and neglected the issues of modal misalignment, geometrical misalignment, and feature selection. In this study, we rethink some essential components in GDSR networks and propose a simple yet effective Dynamic Dual Alignment and Aggregation network (D2A2). D2A2 mainly consists of 1) a dynamic dual alignment module that adapts to alleviate the modal misalignment via a learnable domain alignment block and geometrically align cross-modal features by learning the offset; and 2) a mask-to-pixel feature aggregate module that uses the gated mechanism and pixel attention to filter out irrelevant texture noise from RGB features and combine the useful features with depth features. By combining the strengths of RGB and depth features while minimizing disturbance introduced by the RGB image, our method with simple reuse and redesign of basic components achieves state-of-the-art performance on multiple benchmark datasets. +

+
+ +
+

Method

+ +

+ (a) is an overview of the proposed D2A2 network. + D2A2 consists of two core modules: + the dynamic dual alignment (DDA) module + and the mask-to-pixel feature aggregation (MFA) module. + The backbone of D2A2 is a multi-scale architecture with global skip-connection. + At each scale, Fd and the corresponding Frgb + pass through DDA for modal and geometric alignment, + then the aligned Frgb and Fd + pass through MFA to obtain the effective feature + Fmasked and fuse it with Fd. + (b) and (c) are the specific structures of DDA and MFA, respectively. + DDA comprises a learnable domain alignment block (LDA) + and a dynamic geometrical alignment block (DGA). + MFA comprises a gated convolution (GC) + and a pixel attention (PA) block. +

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+ +
+

+ Visual Comparisons +

+ +

+ +

+ + +
+ +
+

+ BibTex +

+ + 
+                    
+@article{jiang2024the,
+        title={The Devil is in the Details: Boosting Guided Depth Super-Resolution via Rethinking Cross-Modal Alignment and Aggregation},
+        author={Jiang, Xinni and Kuang, Zengsheng and Guo, Chunle and Zhang, Ruixun and Lei Cai and Xiao Fan and Li, Chongyi},
+        journal={arXiv preprint arXiv:2401.08123},
+        year={2024}
+}
+
+
+
+
+

+ Contact +

+

+ Feel free to contact us at xinnijiang@mail.nankai.edu.cn +

+
+
+ + + +
+

© Xin-Ni Jiang | Last updated: Jan. 2024

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