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index 73a851f..f1c4523 100755
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+++ b/docs/index.html
@@ -278,13 +278,13 @@ <h2 class="title is-3">Clearer anytime frame interpolation</h2>
                         <em>
                             <b>When integrating our plug-and-play training strategies ([D,R]) into the state-of-the-art
                                 learning-based models such as
-                                <a href="https://github.com/megvii-research/ECCV2022-RIFE">RIFE</a>,
-                                <a href="https://github.com/ltkong218/IFRNet">IFRNet</a>,
-                                <a href="https://github.com/MCG-NKU/AMT">AMT</a>,
-                                <a href="https://github.com/ltkong218/IFRNet">EMA-VFI</a>,
-                                and others, they exhibit markedly sharper outputs and superior perceptual quality in
-                                arbitrary time interpolations. <br> (Here, we employ RIFE as an illustrative example,
-                                generating 128 interpolated frames using just two images.)
+                                <a href="https://github.com/megvii-research/ECCV2022-RIFE">RIFE [1]</a>,
+                                <a href="https://github.com/ltkong218/IFRNet">IFRNet [2]</a>,
+                                <a href="https://github.com/MCG-NKU/AMT">AMT [3]</a>, and
+                                <a href="https://github.com/ltkong218/IFRNet">EMA-VFI [4]</a>, they exhibit markedly
+                                sharper outputs and superior perceptual quality in arbitrary time interpolations. <br>
+                                (Here, we employ RIFE as an illustrative example, generating 128 interpolated frames
+                                using just two images.)
                             </b>
                         </em>
                     </p>
@@ -490,7 +490,7 @@ <h4 class="title is-5">Editable interpolation</h4>
             <p>
                 <b>Beyond using a uniform index map like time indexing, we can also take advantage of the 2D editable
                     nature of path distance indexing to implement editable frame interpolation techniques.</b>
-                Initially, we can obtain masks for objects of interest using the Segment Anything Model (SAM). We then
+                Initially, we can obtain masks for objects of interest using the Segment Anything Model (SAM) [5]. We then
                 customize the path distance curves for different object regions to achieve manipulated interpolation of
                 anything.
             </p>
@@ -544,6 +544,33 @@ <h2 class="title">Acknowledgements</h2>
     </div>
 </section>
 
+<section class="section" id="reference">
+    <div class="container content is-max-desktop">
+        <h2 class="title">Reference</h2>
+        <p>
+            [1] Huang, Zhewei, Tianyuan Zhang, Wen Heng, Boxin Shi, and Shuchang Zhou. "Real-time intermediate flow
+            estimation for video frame interpolation." In European Conference on Computer Vision, pp. 624-642. Cham:
+            Springer Nature Switzerland, 2022.
+            <br>
+            [2] Kong, Lingtong, Boyuan Jiang, Donghao Luo, Wenqing Chu, Xiaoming Huang, Ying Tai, Chengjie Wang, and Jie
+            Yang. "Ifrnet: Intermediate feature refine network for efficient frame interpolation." In Proceedings of the
+            IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1969-1978. 2022.
+            <br>
+            [3] Li, Zhen, Zuo-Liang Zhu, Ling-Hao Han, Qibin Hou, Chun-Le Guo, and Ming-Ming Cheng. "AMT: All-Pairs
+            Multi-Field Transforms for Efficient Frame Interpolation." In Proceedings of the IEEE/CVF Conference on
+            Computer Vision and Pattern Recognition, pp. 9801-9810. 2023.
+            <br>
+            [4] Zhang, Guozhen, Yuhan Zhu, Haonan Wang, Youxin Chen, Gangshan Wu, and Limin Wang. "Extracting motion and
+            appearance via inter-frame attention for efficient video frame interpolation." In Proceedings of the
+            IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5682-5692. 2023.
+            <br>
+            [5] Kirillov, Alexander, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao,
+            Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick. "Segment Anything." In
+            Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4015-4026. 2023
+        </p>
+    </div>
+</section>
+
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