The creation of new datasets often presents new challenges for video
recognition and can inspire novel ideas while addressing these challenges.
While existing datasets mainly comprise landscape mode videos, our paper seeks
to introduce portrait mode videos to the research community and highlight the
unique challenges associated with this video format. With the growing
popularity of smartphones and social media applications, recognizing portrait
mode videos is becoming increasingly important. To this end, we have developed
the first dataset dedicated to portrait mode video recognition, namely
PortraitMode-400. The taxonomy of PortraitMode-400 was constructed in a
data-driven manner, comprising 400 fine-grained categories, and rigorous
quality assurance was implemented to ensure the accuracy of human annotations.
In addition to the new dataset, we conducted a comprehensive analysis of the
impact of video format (portrait mode versus landscape mode) on recognition
accuracy and spatial bias due to the different formats. Furthermore, we
designed extensive experiments to explore key aspects of portrait mode video
recognition, including the choice of data augmentation, evaluation procedure,
the importance of temporal information, and the role of audio modality.
Building on the insights from our experimental results and the introduction of
PortraitMode-400, our paper aims to inspire further research efforts in this
emerging research area.

通过引入专注于视频识别的首个肖像模式数据集，我们的研究旨在探索这一新兴研究领域，分析肖像模式视频对识别准确性与空间偏差的影响，并在数据增强、评估过程、时间信息和音频模态方面提出了深入的实验，以期激发进一步的研究努力。