Although CLIPScore is a powerful generic metric that captures the similarity
between a text and an image, it fails to distinguish between a caption that is
meant to complement the information in an image and a description that is meant
to replace an image entirely, e.g., for accessibility. We address this
shortcoming by updating the CLIP model with the Concadia dataset to assign
higher scores to descriptions than captions using parameter efficient
fine-tuning and a loss objective derived from work on causal interpretability.
This model correlates with the judgements of blind and low-vision people while
preserving transfer capabilities and has interpretable structure that sheds
light on the caption--description distinction.

通过使用 Concadia 数据集来更新 CLIP 模型，使用参数高效微调和基于因果可解释性的损失目标，以区分描述和标题，并获得盲人和弱视人群的判断，同时保持转移能力和可解释性结构。

更新 CLIP 以偏爱描述而非标题

Updating CLIP to Prefer Descriptions Over Captions

Descriptions are often provided along with recommendations to help users'
discovery. Recommending automatically generated music playlists (e.g.
personalised playlists) introduces the problem of generating descriptions. In
this paper, we propose a method for generating music playlist descriptions,
which is called as music captioning. In the proposed method, audio content
analysis and natural language processing are adopted to utilise the information
of each track.

本研究提出了一种音乐播放列表描述生成方法，使用音频内容分析和自然语言处理技术，称为音乐字幕生成。