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<!-- Title -->
<div class="container">
<!-- <span class="title">Task-Oriented Flow Utilization</span> -->
<!-- <span class="venue">Conference name</span> -->
<span class="title">Visual Med-Alpaca: Bridging Modalities in Biomedical Language Models</span>
<table align="center" border="0" width="1000" class="authors">
<tbody><tr>
<td class="author"> Chang Shu</a><sup>1*</sup></td>
<td class="author"> Baian Chen<sup>2*</sup></td>
<td class="author"> Fangyu Liu</a><sup>1</sup></td>
<td class="author"> Zihao Fu</a><sup>1</sup></td>
<td class="author"> Ehsan Shareghi </a><sup>1</sup></td>
<td class="author"> <a href="https://sites.google.com/site/nhcollier/home/">Nigel Collier</a><sup>1</sup></td>
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<sup>1</sup><a href="https://www.cam.ac.uk/">University of Cambridge</a>
&emsp;&emsp;&emsp;&emsp;
<sup>2</sup>Ruiping Health</a>
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<td><center><img src="files/ltl_logo.jpg" width="1100" ></center></td>
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<tr><td class="caption">Abstract Here</td></tr>
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<br>
<!-- Result -->
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<span class="section-title"> Demo (insert GIF here) (Baian) </span>
</br></br>
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<tr><td><center>
<iframe width="900" height="506" src="xxxx.gif" frameborder="0" allowfullscreen></iframe>
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<tr><td><center>
Please register for Hugging Face and fill out this form [link] to access the online demo of Visual Med-Alpaca. Warning: Only for academic usage and do not apply it to real clinical scenarios!
</center></td></tr>
</table>
</div>
<!-- Abstract -->
<div class="section">
<span class="section-title">Introduction </span>
<p> We introduce Alpaca 7B, a model fine-tuned from the LLaMA 7B model on 52K instruction-following demonstrations. On our preliminary evaluation of single-turn instruction following, Alpaca behaves qualitatively similarly to OpenAI’s text-davinci-003, while being surprisingly small and easy/cheap to reproduce. </p>
<!-- <p class="bibtex">
@article{xue2019video,
title={Video Enhancement with Task-Oriented Flow},
author={Xue, Tianfan and Chen, Baian and Wu, Jiajun and Wei, Donglai and Freeman, William T},
journal={International Journal of Computer Vision (IJCV)},
volume={127},
number={8},
pages={1106--1125},
year={2019},
publisher={Springer}
}
</p> -->
<br>
<b>Assets released:</b></br>
<ul>
<li> Demo: <a href="https://">HuggingFace Space</a>
</li>
<li> Data: <a href="https://github.com/cambridgeltl/">Github</a>
</li>
<li> Data Generation: <a href="https://github.com/cambridgeltl/">Github</a>
</li>
<li> Visual Adaptation: <a href="https://github.com/cambridgeltl/">Github</a>
</li>
<li> Training Code: <a href="https://github.com/cambridgeltl/">Github</a>
</li>
</ul>
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<div class="section">
<span class="section-title"> Overview: Model and Training Recipe </span>
</br></br>
Overview of the model architecture and training procedure.
</div>
<div class="section">
<span class="section-title"> Domain Adaptation: Self-Instruct in Biomedical Domain (Baian) </span>
</br></br>
How to generate the instruct-tuning set
</div>
<div class="section">
<span class="section-title"> Visual Adaptation: Deplot and Medical VQA (Baian)</span>
</br></br>
We also build a large-scale, high-quality video dataset, Vimeo90K. This dataset consists of 89,800 video clips downloaded from <a href='http://toflow.csail.mit.edu/vimeo.com'>vimeo.com</a>, which covers large variaty of scenes and actions. It is designed for the following four video processing tasks: temporal frame interpolation, video denoising, video deblocking, and video super-resolution.
</br></br>
Sampled Frames (Full-resolution samples are <a href="http://data.csail.mit.edu/tofu/dataset.html">here</a>):</br>
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</br></br>
<a name='original_video'></a>
<b><a href="http://toflow.csail.mit.edu/index.html#original_video">The list of original videos</a></b></br></br>
The list of all full-length original videos can be found <a href="http://data.csail.mit.edu/tofu/dataset/original_video_list.txt">here</a>, and <a href="https://rg3.github.io/youtube-dl/">youtube-dl</a> can be used to batch download them. We reused some of utilities by AoT Dataset for scene detection/camera stabilization to generate these video clips and please refer to this <a href="https://github.com/donglaiw/AoT_Dataset">repository</a> for more details.</br></br>
We further process these 89,800 video clips to generate the following two subsets.</br></br>
<a name='triplet'></a>
<b><a href="http://toflow.csail.mit.edu/index.html#triplet">Triplet dataset (for temporal frame interpolation):</a></b></br></br>
The triplet dataset consists of 73,171 3-frame sequences with a fixed resolution of 448 x 256, extracted from 15K selected video clips from Vimeo-90K. This dataset is designed for temporal frame interpolation. Download links are
<ul>
<li> Testing set only (17GB): <a href="http://data.csail.mit.edu/tofu/testset/vimeo_interp_test.zip">zip</a> </li>
<li> Both training and test set (33GB): <a href="http://data.csail.mit.edu/tofu/dataset/vimeo_triplet.zip">zip</a> </li>
</ul>
<a name='septuplet'></a>
<b><a href="http://toflow.csail.mit.edu/index.html#septuplet">Septuplet dataset (for video denoising, deblocking, and super-resoluttion):</a></b></br></br>
<b> Notice: we have recently updated our testing denoising dataset to fix a bug in denoising test data generation. The new quantitative result of our algorithm is reported in <a href="http://toflow.csail.mit.edu/toflow_ijcv.pdf">our updated paper</a> </b></br></br>
The septuplet dataset consists of 91,701 7-frame sequences with fixed resolution 448 x 256, extracted from 39K selected video clips from Vimeo-90K. This dataset is designed to video denoising, deblocking, and super-resolution.
<ul>
<li> The test set for video denoising (16GB): <a href="http://data.csail.mit.edu/tofu/testset/vimeo_denoising_test_20191102.zip">zip</a> </li>
<li> The test set for video deblocking (11GB): <a href="http://data.csail.mit.edu/tofu/testset/vimeo_deblocking_test.zip">zip</a> </li>
<li> The test set for video super-resolution (6GB): <a href="http://data.csail.mit.edu/tofu/testset/vimeo_super_resolution_test.zip">zip</a> </li>
<li> The original test set (not downsampled or downgraded by noise) (15GB): <a href="http://data.csail.mit.edu/tofu/testset/vimeo_test_clean.zip">zip</a> </li>
<li> The original training + test set (82GB): <a href="http://data.csail.mit.edu/tofu/dataset/vimeo_septuplet.zip">zip</a> </li>
</ul>
<!--<b>Description of Vimeo-90K Dataset:&nbsp;</b><a href="https://github.com/anchen1011/toflow#the-vimeo-dataset">github</a> </br>-->
</div>
<!-- div class="section">
<span class="section-title">Results </span></br>
<p class="subsection">Interpolation</p -->
<!-- Result start -->
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<!-- p class="subsection">Visualization</p -->
<!-- Visualization start -->
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<!-- /div -->
<div class="section">
<span class="section-title"> Implementation Details </span>
</br></br>
Hyper-parameter
Training time
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<div class="section">
<span class="section-title"> Evaluation and Known Limitations </span>
</br></br>
We evaluate
Limited Human evaluation (Links Here)
</div>
<div class="section">
<span class="section-title"> Comparison with Other Methods </span>
</br></br>
Compare with ChatGPT / Alpaca / Galactica
</div>
<div class="section">
<span class="section-title"> Future Work </span>
</br></br>
Compare with ChatGPT / Alpaca / Galactica
</div>
<div class="section">
<span class="section-title"> Acknowledgement </span>
</br></br>
Thanks to
</div>
<p>&nbsp;</p>
<!-- end .container --></div>
</body></html>
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