Updated citation information

Author: RLado

CITATION.cff

@@ -0,0 +1,55 @@
+cff-version: 1.2.0
+title: 'STB-VMM: Swin Transformer Based Video Motion Magnification'
+message: >-
+  If you use this software, please cite it using the
+  metadata from this file.
+type: software
+authors:
+  - given-names: Ricard
+    family-names: Lado-Roigé
+    email: ricardlador@iqs.edu
+    affiliation: >-
+      IQS School of Engineering, Universitat Ramon Llull,
+      Via Augusta 390, 08017 Barcelona, Spain
+    orcid: 'https://orcid.org/0000-0002-6421-7351'
+  - given-names: Marco A.
+    family-names: Pérez
+    orcid: 'https://orcid.org/0000-0003-4140-1823'
+    affiliation: >-
+      IQS School of Engineering, Universitat Ramon Llull,
+      Via Augusta 390, 08017 Barcelona, Spain
+identifiers:
+  - type: doi
+    value: 10.48550/arXiv.2302.10001
+    description: >-
+      STB-VMM: Swin Transformer Based Video Motion
+      Magnification
+repository-code: 'https://github.com/RLado/STB-VMM'
+abstract: >-
+  The goal of video motion magnification techniques is to
+  magnify small motions in a video to reveal previously
+  invisible or unseen movement. Its uses extend from
+  bio-medical applications and deep fake detection to
+  structural modal analysis and predictive maintenance.
+  However, discerning small motion from noise is a complex
+  task, especially when attempting to magnify very subtle
+  often sub-pixel movement. As a result, motion
+  magnification techniques generally suffer from noisy and
+  blurry outputs. This work presents a new state-of-the-art
+  model based on the Swin Transformer, which offers better
+  tolerance to noisy inputs as well as higher-quality
+  outputs that exhibit less noise, blurriness and artifacts
+  than prior-art. Improvements in output image quality will
+  enable more precise measurements for any application
+  reliant on magnified video sequences, and may enable
+  further development of video motion magnification
+  techniques in new technical fields. 
+keywords:
+  - Computer vision
+  - Deep Learning
+  - Swin Transformer
+  - Motion Magnification
+  - Image Quality Assessment
+license: MIT
+version: v1.0.0
+date-released: '2022-07-12'

README.md

@@ -6,7 +6,7 @@
 
 ---
 
-This repository contains the official implementation of the [STB-VMM: Swin Transformer Based Video Motion Magnification](https://www.iqs.edu/en 'paper') paper in PyTorch.
+This repository contains the official implementation of the [STB-VMM: Swin Transformer Based Video Motion Magnification](https://arxiv.org/abs/2302.10001 'paper') paper in PyTorch.
 
 The goal of Video Motion Magnification techniques is to magnify small motions in a video to reveal previously invisible or unseen movement. Its uses extend from bio-medical applications and deep fake detection to structural modal analysis and predictive maintenance. However, discerning small motion from noise is a complex task, especially when attempting to magnify very subtle often sub-pixel movement. As a result, motion magnification techniques generally suffer from noisy and blurry outputs. This work presents a new state-of-the-art model based on the Swin Transformer, which offers better tolerance to noisy inputs as well as higher-quality outputs that exhibit less noise, blurriness and artifacts than prior-art. Improvements in output image quality will enable more precise measurements for any application reliant on magnified video sequences, and may enable further development of video motion magnification techniques in new technical fields.
 
@@ -52,11 +52,11 @@ https://user-images.githubusercontent.com/25719985/194240973-8d93968f-283b-4802-
 ## Citation
 ```bibtex
 @article{lado2022_STB-VMM,
-  title={STB-VMM: Swin Transformer Based Video Motion Magnification},
-  doi = {},
-  author={Lado-Roigé, Ricard and P{\'{e}}rez, Marco A.},
-  journal={Knowledge-Based Systems},
-  year={2022},
+  title = {STB-VMM: Swin Transformer Based Video Motion Magnification},
+  doi = {10.48550/arXiv.2302.10001},
+  author = {Lado-Roig{\'{e}}, Ricard and P{\'{e}}rez, Marco A.},
+  journal = {Knowledge-Based Systems},
+  year = {2022},
   note = { (Under review) }
 }
 ```