文章目录

• Multimodal Transformer for Unaligned Multimodal Language Sequences
• • Summary
  • 摘要 (中文)
  • Research Objective
  • Background and Problems
  • main work
  • Related work
  • Method(s)
  • Conclusion
  • Reference(optional)
  • Arouse for me

Multimodal Transformer for Unaligned Multimodal Language Sequences

A类会议的论文。ACL

Summary

The author wants to infer how we combine voice with a face. In this paper, the author does many work base on VGGFace and VoxCeleb database. Its main contributions can be summarized as follow :

1. introduce CNN for binary or multi-way’s face matching with audio.
2. Using different audio to identify the dynamic speaker.
3. the author discovers that CNN matches human performance on easy examples (different gender). But it exceeds human judgment in complex examples. (face has the same gender, age, and nationality)

摘要 (中文)

摘要在这篇论文中，我们研究人脸和声音之间的联系。视听整合，特别是面部和声音信息的整合，是神经科学研究的一个重要领域。结果表明，两种模式之间的重叠信息在说话人识别等知觉任务中起着重要作用。通过对我们创建的新数据集进行在线研究，我们证实了之前的发现，即人们可以将看不见的面孔与相应的声音联系起来，反之亦然，其准确性大于随机概率。我们对人脸和声音之间的重叠信息进行了计算建模，并表明学习的交叉模态表示包含了足够的信息来识别匹配的人脸和声音，其性能与人类相似。我们的表现与特定的人口统计属性和特征的相关性，从视觉或听觉模态单独获得。我们发布了我们研究中使用的人们朗读短文本的视听记录和人口统计注释数据集。

Research Objective

We examine whether faces and voices encode redundant identity information and measure to which extent.

Background and Problems

• Background

  • We humans often deduce various, albeit perhaps crude, information from the voice of others, such as gender, approximate age and even personality.

• previous methods brief introduction

  • Neuroscientists have observed that the multimodal associations of faces and voices play a role in perceptual tasks such as speaker recognition [19,14,44].

• Problem Statement

  • not state in introduction

main work

1. We provide an extensive human-subject study, with both the participant pool and dataset larger.
2. We learn the co-embedding of modal representations of human faces and voices, and evaluate the learned representations extensively, revealing unsupervised correlations to demographic, prosodic, and facial features.
3. We present a new dataset of the audiovisual recordings of speeches by 181 individuals with diverse demographic background, totaling over 3 hours of recordings, with the demographic annotations.

work limitations : self dataset is not big enough.

Related work

• Human capability for face-voice association:

  • . The study of Campanella and Belin [5] reveals that humans leverage the interface between facial and vocal information for both person recognition and identity processing.
    …

• Audiovisual cross-modal learning by machinery:

  • Nagrani et al. [25] recently presented a computational model for the facevoice matching task. While they see it as a binary decision problem, we focus more on the shared information between the two modalities and extract it as a representation vector residing in the shared latent space, in which the task is modeled as a nearest neighbor search.
    …

Method(s)

• Methods one : Study on Human Performance

  • 1.Participants were presented with photographs of two different models and a 10-second voice recording of one of the models. They were asked to choose one and only one of the two faces they thought would have a similar voice to the recorded voice (V → F).
  • 2. dataset : Amazon Mechanical Turk ( the participants fill out a survey about their fender,age, and soon on)
  • 3. The result show that participants were able to match a voice of an unfamiliar person to a static facial image of the same person at better than chance levels.
       

• Cross-modal Metric Learning on Faces and Voices

  • 1. Our attempt to learn cross-modal representations between faces and voices is inspired by the significance of the overlapping information in certain cognitive tasks like identity recognition, as discussed earlier.
  • 2. dataset : We use the VoxCeleb dataset [26] to train our network. From each clip, the first frame and first 10 seconds of the audio are used, as the beginning of the clips is usually well aligned with the beginning of utterances.
  • 3. Network : we use VGG16 [33] and SoundNet [2], which have shown sufficient model capacities while allowing for stable training in a variety of applications.
  • 4. The result show that
       

Conclusion

• main controbution

1. first, with human subjects, showing the baseline for how well people perform such tasks.
2. On machines using deep neural networks, demonstrating that machines perform on a par with humans.

• week point

1. However, we emphasize that, similar to lie detectors, such associations should not be used for screening purposes or as hard evidence. Our work suggests the possibility of learning the associations by referring to a part of the human cognitive process, but not their definitive nature, which we believe would be far more complicated than it is modeled as in this work.

• further work

1. not reflected .

Reference(optional)

Arouse for me

• This paper related work and introduction is worthed to study。 Because it is not hard to read and reasonable. However, In experiment and method part, It is hard to understand by me.
• this paper has self dataset, but it publish version is not able to download. So , I can’t rechieve it.
• 中文解读： https://blog.csdn.net/weixin_44390691/article/details/105182181?utm_medium=distribute.pc_relevant.none-task-blog-title-2&spm=1001.2101.3001.4242

标签：information,Transformer,Language,work,dataset,Multimodal,human,voice
来源： https://blog.csdn.net/liupeng19970119/article/details/113784089