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<!DOCTYPE html>
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<title>FairQueue: Rethinking Prompt Learning for Fair Text-to-Image Generation (NeurIPS24)</title>
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<h1 class="title is-1 publication-title">NeurIPS24: FairQueue: Rethinking Prompt Learning for Fair Text-to-Image Generation</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="-">Christopher T. H. Teo</a><sup>1</sup>,</span>
<span class="author-block">
<a href="-">Milad Abdollahzadeh</a><sup>1</sup>,</span>
<span class="author-block">
<a href="-">Xinda Ma</a>,</span>
<span class="author-block">
<a href="-">Ngai-Man Cheung</a><sup>*</sup>,
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>Singapore Univeristy of Technology and Design (SUTD),</span>
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<span class="dnerf">Nerfies</span> turns selfie videos from your phone into
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<h2 class="title is-3">Abstract</h2>
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<p>
Recently, prompt learning has emerged as the state-of-the-art (SOTA) for fair text-
to-image (T2I) generation. Specifically, this approach leverages readily available
reference images to learn inclusive prompts for each target Sensitive Attribute (tSA),
allowing for fair image generation. In this work, we first reveal that this prompt
learning-based approach results in degraded sample quality. Our analysis shows that
the approach’s training objective–which aims to align the embedding differences of
learned prompts and reference images–could be sub-optimal, resulting in distortion
of the learned prompts and degraded generated images.
To further substantiate this claim, as our major contribution, we deep dive into
the denoising subnetwork of the T2I model to track down the effect of these learned
prompts by analyzing the cross-attention maps. In our analysis, we propose novel
prompt switching analysis: I2H and H2I. Furthermore, we propose new quantitative
characterization of cross-attention maps. Our analysis reveals abnormalities in
the early denoising steps, perpetuating improper global structure that results in
degradation in the generated samples. Building on insights from our analysis, we
propose two ideas: (i) Prompt Queuing and (ii) Attention Amplification to address
the quality issue. Extensive experimental results on a wide range of tSAs show
that our proposed method outperforms SOTA approach’s image generation quality,
while achieving competitive fairness.
</p>
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As a byproduct of our method, we can also solve the matting problem by ignoring
samples that fall outside of a bounding box during rendering.
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class="interpolation-image"
alt="Interpolation end reference image."/>
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<br/> -->
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viewpoint such as a stabilized camera by playing back the training deformations.
</p>
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controls
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playsinline
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type="video/mp4">
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</div> -->
<!--/ Re-rendering. -->
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<!-- / Animation. -->
<!-- Overview -->
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<h2 class="title is-3">Overview</h2>
<div class="content has-text-justified">
<center>
<table align="center" width="880px">
<tbody><tr>
<td width="260px">
<center>
<img class="round" style="width:880px" src="./resources/Fig_1_test_v3_compressed.png">
</center>
</td>
</tr>
</tbody></table>
<table align="center" width="880px">
<tbody><tr>
<td>
<p style="text-align:justify; text-justify:inter-ideograph;">
</p><h4 class="title is-5">Contributions</h4>
We examine SOTA prompt learning methods that utilize directional alignment between prompt embeddings and reference image embeddings in fair T2I generation. Our study reveals two key issues:
<ul>
<li>The generation of a moderate number of samples with degraded quality.</li>
<li>The potential for noisy reference image embeddings, as they may capture unrelated concepts beyond the target Sensitive Attribute (tSA), resulting in sub-optimal learning.</li>
</ul>
To address this, we propose a novel analysis framework (H2I/I2H) that scrutinizes the cross-attention maps during the denoising process of T2I generation. The analysis highlights abnormalities in the learned prompts, particularly in the early denoising steps. Based on these insights, we introduce FairQueue, an improved method that addresses quality issues while maintaining competitive fairness.
<!--
<b>1: </b>
We conduct a study to reveal that even highly-accurate SA classifiers could still incur significant
fairness measurement errors when using existing framework.
<br> <br>
<b>2: </b>
To enable evaluation of fairness measurement frameworks, we propose new datasets based on
generated samples from StyleGAN, StyleSwin and SDM, with manual labeling w.r.t. SA
<br> <br>
<b>3: </b>
We propose a new and accurate fairness measurement framework, CLEAM, that accounts for SA
classifier inaccuracies and provides point and interval estimates
<br> <br>
<b>4: </b>
Using CLEAM, we reveal considerable biases in several important generative models, prompting
careful consideration when applying them for different applications.
<br>
-->
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<!--/ Overview -->
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<pre><code>
@misc{teo2024fairqueuerethinkingpromptlearning,
title={FairQueue: Rethinking Prompt Learning for Fair Text-to-Image Generation},
author={Christopher T. H Teo and Milad Abdollahzadeh and Xinda Ma and Ngai-man Cheung},
year={2024},
eprint={2410.18615},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2410.18615},
}
</code></pre>
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