本文探讨了细粒度动作识别领域的挑战，特别是环境偏见问题，提出了利用多模态自监督和模态对抗训练的解决方案。通过引入一种新的数据集，文章强调了未来工作在音频模态上的潜在研究方向。
Abstract
Fine-grained action recognition datasets exhibit environmental bias, where multiple video sequences are captured from a limited number of environments. Multi-modal nature of video（视频的多模态性），提出的方法一个是multi-modal self-supervision，还有一个是adversarial training per modality
Introduction
fine-grained action recognition,
Few works have attempted deep UDA for video data《Temporal attentive alignment for large-scale video domain adaptation, ICCV2019》《Deep domain adaptation in action space, BMVC2018》
Conclusion
modality指的是两种信息（optical flow和RGB信息）,future work包含audio
Key points: Motivation很好; 提出的新数据集
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