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<h1 class="title is-1 publication-title">MMCosine: Multi-Modal Cosine Loss Towards Balanced Audio-Visual Fine-Grained Learning</h1>
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<a href="https://rick-xu315.github.io/">Ruize Xu</a><sup>1</sup>,</span>
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<a href="https://gewu-lab.github.io/MMCosine/">Ruoxuan Feng</a><sup>1</sup>,</span>
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<a href="https://scholar.google.com/citations?hl=zh-CN&user=4nGncN4AAAAJ">Shi-xiong Zhang</a><sup>2</sup>,
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<a href="https://dtaoo.github.io/">Di Hu</a><sup>1</sup>,
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<span class="author-block"><sup>1</sup>Gaoling School of Artificial Intelligence, Renmin University of China,</span><br/>
<span class="author-block"><sup>2</sup>Tencent AI Lab</span>
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<h2 class="title is-3">Abstract</h2>
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<p>
Audio-visual learning helps to comprehensively understand the world by fusing practical information from multiple modalities. However, recent studies show that the imbalanced optimization of uni-modal encoders in a joint-learning model is a bottleneck
to enhancing the model`s performance. We further find that the up-to-date imbalance-mitigating methods fail on some audio-visual fine-grained tasks, which have a higher demand for distinguishable feature distribution.
</p>
<p>
Fueled by the success of cosine loss that builds hyperspherical feature spaces and achieves lower intra-class angular variability, this paper proposes Multi-Modal Cosine loss, <span class="dnerf">MMCosine</span>. It performs
a modality-wise $L_2$ normalization to features and weights towards balanced and better multi-modal fine-grained learning. We demonstrate that our method can alleviate the imbalanced optimization from the perspective of weight
norm and fully exploit the discriminability of the cosine metric.
</p>
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Extensive experiments prove the effectiveness of our method and the versatility with advanced multi-modal fusion strategies and up-to-date imbalance-mitigating methods.
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<b>(a,b)</b> In the end-to-end training of an audio-visual concatenation-based network for classification, the dominant audio modality rapidly handles the overall model performance and the joint logit scores, while the visual modality
keeps under-optimized.
<b>(c,d)</b> Further tracking on modality-wise norm of weight vectors indicates the easily-trained audio encoder tends to have its weight in norm growing much faster than the weak visual modality.
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<h2 class="subtitle has-text-justified">
<p> To deal with the above problem, we propose a multi-modal cosine loss, <b>MMCosine</b>. The main steps are <b>(a)</b> Modality-wise normalization of weight and feature to mitigate the imbalance and <b>(b)</b>scaling with hyperparameter
$s$ to guarantee the convergence.
</p>
<p>
We also give the lower bound of $s$ given expected posterior probability $p$ of ground-truth label and the number of total labels $C$. The demonstration can be found in the supplementary material. $$s\geq \frac{C-1}{2(C+1)}log\frac{(C-1)p}{1-p} $$
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$\dagger$ indicates MMCosine is applied. Combined with MMCosine, most of the fusion methods gain considerable improvement for datasets of various scales, domains, and label amount.
</h2>
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<h2 class="title is-4">Gap Mitigation</h2>
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The performance gap of uni-modal encoders is reduced by MMCosine, with the weak modality and the joint model boosted.
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<h2 class="title is-4">Cosine discriminability</h2>
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The learned angles between uni-modal features and ground-truth class centers become more compact. MMCosine can lower the intra-class angular variation and maximize the discriminability of cosine metric.
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<pre><code>@article{ruize2023mmcosine,
author={Ruize, Xu and Ruoxuan, Feng and Shi-xiong, Zhang, and Di, Hu},
booktitle={ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
year={2023},
organization={IEEE},
}</code></pre>
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