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<!DOCTYPE html>
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<head>
<meta charset="utf-8">
<!-- <meta name="description"
content="HyperNeRF handles topological variations by modeling a family of shapes in a higher-dimensional space, thereby producing more realistic renderings and more accurate geometric reconstructions.">
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<title>DSL-FIQA: Assessing Facial Image Quality via Dual-Set Degradation Learning and Landmark-Guided Transformer
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<img src="static/images/logo.svg" alt="HyperNeRF"/>
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</div> -->
<div class="container has-text-centered">
<h1 class="title is-1 publication-title">
RobustSAM: Segment Anything Robustly on Degraded Images
</h1>
<div class="is-size-5 publication-authors">
<div class="author-block"><a href="https://github.com/robustsam/RobustSAM">Wei-Ting Chen</a> <sup>1</sup></div>
<div class="author-block"><a href="https://github.com/robustsam/RobustSAM"> Yu-Jiet Vong</a> <sup>1</sup></div>
<div class="author-block"><a href="https://github.com/robustsam/RobustSAM"> Sy-Yen Kuo</a> <sup>1</sup></div>
<div class="author-block"><a href="https://github.com/robustsam/RobustSAM"> Sizhou Ma</a> <sup>2</sup></div>
<div class="author-block"><a href="https://github.com/robustsam/RobustSAM"> Jian Wang</a> <sup>2</sup></div>
<!-- <div class="author-block">
<a href="https://utkarshsinha.com">Utkarsh Sinha</a><sup>2</sup></div>
<div class="author-block"> -->
<!-- <a href="https://phogzone.com/">Peter Hedman</a><sup>2</sup></div> -->
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>National Taiwan University,</span>
<span class="author-block"><sup>2</sup>Snap Inc.</span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
<!-- PDF Link. -->
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<a href="https://github.com/robustsam/RobustSAM"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-file-pdf"></i>
</span>
<span>Paper</span>
</a>
</span>
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<a href="https://github.com/robustsam/RobustSAM"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
</a>
</span>
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<a href="https://github.com/robustsam/RobustSAM"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
</span>
<!-- Dataset Link. -->
<span class="link-block">
<a href="https://github.com/robustsam/RobustSAM"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="far fa-images"></i>
</span>
<span>Data</span>
</a>
</span>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="hero teaser">
<div class="hero-body">
<div class="container is-max-desktop">
<!-- <video id="teaser" autoplay controls muted loop playsinline height="100%">
<source src="./static/images/teaser.mp4"
type="video/mp4">
</video> -->
<!-- <h2 class="subtitle has-text-centered"> -->
<!-- <i>RobustSAM</i> outperfroms SAM in various degradations. -->
<!-- <i>RobustSAM</i> handles topological variations by modeling a
family of shapes in a higher-dimensional space, thereby producing more realistic renderings
and more accurate geometric reconstructions. -->
<!-- </h2> -->
</div>
</div>
</section>
<!-- <section class="hero is-dark is-small">
<div class="hero-body">
<div class="hero is-dark is-small"">
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<!-- <div>
<div class="results-item">
<video poster="" id="espresso-rgb" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/espresso_rgb.mp4"
type="video/mp4">
</video>
<video poster="" id="espresso-depth" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/espresso_depth.mp4"
type="video/mp4">
</video>
</div>
</div> -->
<!-- <div>
<div class="results-item">
<video poster="" id="split-cookie-rgb" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/split-cookie_rgb.mp4"
type="video/mp4">
</video>
<video poster="" id="split-cookie-depth" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/split-cookie_depth.mp4"
type="video/mp4">
</video>
</div>
</div> -->
<!-- <div>
<div class="results-item">
<video poster="" id="3dprinter-rgb" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/3dprinter_rgb.mp4"
type="video/mp4">
</video>
<video poster="" id="3dprinter-depth" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/3dprinter_depth.mp4"
type="video/mp4">
</video>
</div>
</div>
<div>
<div class="results-item">
<video poster="" id="ricardo-rgb" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/ricardo_rgb.mp4"
type="video/mp4">
</video>
<video poster="" id="ricardo-depth" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/ricardo_depth.mp4"
type="video/mp4">
</video>
</div>
</div>
<div>
<div class="results-item">
<video poster="" id="americano-rgb" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/americano_rgb.mp4"
type="video/mp4">
</video>
<video poster="" id="americano-depth" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/americano_depth.mp4"
type="video/mp4">
</video>
</div>
</div> -->
<!-- <img src="./static/figures/level_set/0.svg"/> -->
<!-- <img src="./robustsam_file/gif_output/blur.gif"/>
<img src="./robustsam_file/gif_output/haze_back_n_forth.gif"/>
<img src="./robustsam_file/gif_output/lowlight_back_n_forth.gif"/>
<img src="./robustsam_file/gif_output/rain_back_n_forth.gif"/> -->
<div class="img-container">
<img class="custom-gif" src="./file/overview.pdf">
<!-- <img class="custom-gif" src="./robustsam_file/gif_output/haze_back_n_forth.gif"> -->
</div>
<!-- <div class="img-container">
<img class="custom-gif" src="./robustsam_file/gif_output/lowlight_back_n_forth.gif">
<img class="custom-gif" src="./robustsam_file/gif_output/rain_back_n_forth.gif"> -->
</div>
<!-- </div> -->
<div class="columns is-centered has-text-centered">
<div class="column is-three-quarters">
<p>
<i>RobustSAM </i> outperforms with precise boundaries and
intact structures, where SAM falters with errors and fragmentation.
Red star points and bounding boxes are our examples' input prompts.
Click on the arrows or drag to see more results.
</p>
</div>
</div>
<!-- </div>
</div>
</section> -->
<section class="section">
<div class="container is-max-desktop">
<!-- Abstract. -->
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Generic Face Image Quality Assessment (GFIQA) evaluates the perceptual quality of facial images, which is crucial in improving image restoration algorithms and selecting high-quality face images for downstream tasks.
We present a novel transformer-based method for GFIQA, which is aided by two unique mechanisms.
First, a novel Dual-Set Degradation Representation Learning (DSL) mechanism uses facial images with both synthetic and real degradations to decouple degradation from content, ensuring generalizability to real-world scenarios. This self-supervised method learns degradation features on a global scale, providing a robust alternative to conventional methods that use local patch information in degradation learning.
Second, our transformer leverages facial landmarks to emphasize visually salient parts of a face image in evaluating its perceptual quality.
</p>
<p>
We also introduce a balanced and diverse Comprehensive Generic Face IQA (CGFIQA-40k) dataset of 40K images carefully designed to overcome the biases, in particular the imbalances in skin tone and gender representation, in existing datasets.
Extensive analysis and evaluation demonstrate the robustness of our method, marking a significant improvement over prior methods.
</p>
<!-- <p>
We address this limitation by lifting
NeRFs into a higher dimensional space, and by representing the 5D radiance field
corresponding to each individual input image as a slice through this "hyper-space". Our
method is inspired by level set methods, which model the evolution of surfaces as slices
through a higher dimensional surface. We evaluate our method on two tasks: (i)
interpolating smoothly between "moments", i.e., configurations of the scene, seen in the
input images while maintaining visual plausibility, and (ii) novel-view synthesis at
fixed moments. We show that our method, which we dub HyperNeRF, outperforms existing
methods on both tasks. Compared to Nerfies, <i>HyperNeRF</i> reduces average error rates by
4.1% for interpolation and 8.6% for novel-view synthesis, as measured by LPIPS.
</p> -->
</div>
</div>
</div>
<!--/ Abstract. -->
<!-- Paper video. -->
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-2">Video</h2>
<div class="publication-video">
<iframe width="640" height="480" src="https://www.youtube.com/watch?v=bx0He5eE8fE"
title="YouTube video player" frameborder="0"
allow="accelerometer; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
allowfullscreen></iframe>
</div>
</div>
</div>
<!--/ Paper video. -->
</div>
</section>
<hr/>
<section class="section">
<!-- <div class="container is-max-desktop">
<h2 class="title is-3">Motivation</h2>
<div class="columns is-centered">
<!-- Level Sets. -->
<!-- <div class="column is-half">
<div class="content">
<h3 class="title is-4">Level-Set Methods</h3>
<div class="level-set has-text-justified">
<p>
HyperNeRF represents changes in scene topology by providing a
NeRF with a higher-dimensional input. This is inspired by level-set methods.
Level-set methods provide a means to model a family of topologically-varying shapes as
slices of a higher dimensional auxiliary function. For example, these shapes
</p>
<div class="level-set-shapes">
<img src="./static/figures/level_set/0.svg"/>
<img src="./static/figures/level_set/1.svg"/>
<img src="./static/figures/level_set/2.svg"/>
</div>
<p>
can be represented as slices through this auxiliary shape
</p>
<model-viewer class="level-set-slices"
src="./static/figures/level_set/level_set_3d.glb"
alt="Slices through a 3D ambient surface."
environment-image="neutral" auto-rotate camera-controls></model-viewer>
<p>
We can naturally model topologically varying shapes by just moving the
slicing plane along the higher dimensions. For example, this animation was generated
by
moving the slicing plane from top to bottom:
</p>
<div class="has-text-centered">
<video class="level-set-interpolate" controls autoplay loop playsinline muted height="100%">
<source src="./static/figures/level_set/interpolate2.mp4" type="video/mp4">
</video>
</div>
</div>
</div>
</div> -->
<!--/ Level Sets. -->
<!-- <div class="column is-half">
<div class="content has-text-justified">
<h3 class="is-4">Slicing Surfaces</h3>
<p>
Consider the follow shapes, which have different permutations of O xand X.
</p>
<div class="level-set-ox-shapes">
<img src="./static/figures/level_set/ox/0.svg"/>
<img src="./static/figures/level_set/ox/1.svg"/>
<img src="./static/figures/level_set/ox/2.svg"/>
<img src="./static/figures/level_set/ox/3.svg"/>
</div>
<p>
Traditionally, level-set methods use straight planes to slice the higher-dimensional
surface:
</p>
<model-viewer class="level-set-slices"
src="./static/figures/level_set/ox/ox_ap.glb"
alt="Slices through a 3D ambient surface."
environment-image="neutral" auto-rotate camera-controls></model-viewer>
<p>
This means the higher-dimensional shape must contain copies of the same shape since
each permutation has to lie along a single straight slice through the z-axis. If we let
the slicing plane bend, it results in a much cleaner template:
</p>
<model-viewer class="level-set-slices"
src="./static/figures/level_set/ox/ox_ds.glb"
alt="Slices through a 3D ambient surface."
environment-image="neutral" auto-rotate camera-controls></model-viewer>
<p>Please see the paper for details.</p>
</div>
</div>
</div>
</div> --> -->
<div class="container is-max-desktop">
<div class="content has-text-justified">
<h2 class="title is-3">Architecture of RobustSAM</h2>
<p>
The key contribution of RobustSAM
is the <b>Anti-Degradation Output Token Generation (AOTG)</b> and <b>Anti-Degradation Mask Feature Generation
(AMFG)</b> modules, which extract degradation-invariant information that is aligned those extracted from
clear images by the original SAM.
</p>
<div class="has-text-centered">
<!-- <embed src="./robustsam_file/architecture.pdf" width="800px" height="2100px" /> -->
<img style="width: 100%;" src="./file/DCE_v7.pdf"
alt="DCE architecture."/>
</div>
<!-- <h3 class="title is-4">Overview of the proposed AMFG and AOTG modules</h3>
<!-- <embed src="./robustsam_file/style-suppresion.pdf" width="800px" height="2100px" /> -->
<img style="width: 100%;" src="./robustsam_file/module.jpg"
alt="AMFG and AOTG modules."/> -->
<!-- <p>
HyperNeRF leverages main idea of level set methods by using a template NeRF which lives in
higher dimensions. In addition to the spatial coordinates (X, Y, Z), the NeRF MLP takes
additional higher dimensional coordinates W<sub>1</sub> and W<sub>2</sub>. We call these
the "ambient dimensions".
</p>
<p>
Here is an interactive viewer for the hyper-space of capture shown in the teaser. Drag the
<span style="color: #29e">blue cursor</span> around to change the ambient dimension rendered
on the right.
</p> -->
</div>
<!-- <div class="columns is-centered">
<div class="column is-half">
<div class="hyper-space-wrapper has-text-centered">
<div class="hyper-space-axis">
<div class="hyper-space">
<div class="hyper-space-cursor"></div>
</div>
</div>
Ambient Dimension Coordinates
<br/>
<small>(Background shows log density of coordinate)</small>
</div>
</div>
<div class="column is-half has-text-centered">
<div class="hyper-grid-wrapper">
<div class="hyper-grid-rgb">
<img src="./static/figures/hyper_grid.jpg"/>
</div>
</div>
The hyper-space template rendered from a fixed viewpoint.
</div>
</div> -->
</div>
</section>
<hr/>
<section class="section" id="BibTeX">
<div class="container content is-max-desktop">
<h2 class="title">BibTeX</h2>
<pre><code>@inproceedings{chen2024robustsam,
author = {Chen, Wei-Ting and Krishnan, Gurunandan and Gao, Qiang and Kuo, Sy-Yen and Ma, Sizhou and Wang, Jian},
title = {RobustSAM: Segment Anything Robustly on Degraded Images},
journal = {CVPR},
issue_date = {2024}
}</code></pre>
</div>
</section>
<!-- <section class="section" id="acknowledgements">
<div class="container content is-max-desktop">
<h2 class="title">Acknowledgements</h2>
<p>Special thanks to <a href="https://homes.cs.washington.edu/~holynski/">Aleksander Hołyński</a>,
<a href="https://roxanneluo.github.io/">Xuan Luo</a>, and Haley Cho for their support and
help with collecting data. Thanks to <a href="https://zhengqili.github.io/">Zhengqi Li</a> and
<a href="http://www.oliverwang.info/">Oliver Wang</a> for their help with the NSFF experiments.</p>
</div>
</section> -->
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