Musicians and fans often produce lyric videos, a form of music videos that
showcase the song's lyrics, for their favorite songs. However, making such
videos can be challenging and time-consuming as the lyrics need to be added in
synchrony and visual harmony with the video. Informed by prior work and close
examination of existing lyric videos, we propose a set of design guidelines to
help creators make such videos. Our guidelines ensure the readability of the
lyric text while maintaining a unified focus of attention. We instantiate these
guidelines in a fully automated pipeline that converts an input music video
into a lyric video. We demonstrate the robustness of our pipeline by generating
lyric videos from a diverse range of input sources. A user study shows that
lyric videos generated by our pipeline are effective in maintaining text
readability and unifying the focus of attention.

我们提出了一套设计指南，帮助创作者制作抒情视频，以确保歌词文本的可读性，并保持注意力的统一焦点。我们通过一个完全自动化的流程将输入音乐视频转化为抒情视频，并通过多样化的输入来源生成的抒情视频的用户研究结果表明，我们的流程能够有效地保持文本可读性和注意力的统一焦点。

音乐视频自动转换为歌词视频

Automated Conversion of Music Videos into Lyric Videos

Trojan attacks raise serious security concerns. In this paper, we investigate
the underlying mechanism of Trojaned BERT models. We observe the attention
focus drifting behavior of Trojaned models, i.e., when encountering an poisoned
input, the trigger token hijacks the attention focus regardless of the context.
We provide a thorough qualitative and quantitative analysis of this phenomenon,
revealing insights into the Trojan mechanism. Based on the observation, we
propose an attention-based Trojan detector to distinguish Trojaned models from
clean ones. To the best of our knowledge, this is the first paper to analyze
the Trojan mechanism and to develop a Trojan detector based on the
transformer's attention.

本文提出了一种基于 transformer 注意机制的 Trojan 检测器，探究了 Trojan BERT 模型的机制，发现了 Trojan 模型注视焦点漂移的行为。