标题： MAMS：视频描述的模型无关模块选择框架 显示英文标题

标题： MAMS: Model-Agnostic Module Selection Framework for Video Captioning

摘要： 多模态变压器在视频字幕任务中正迅速获得关注。 现有的多模态视频字幕方法通常提取固定数量的帧，这带来了关键挑战。 当提取有限数量的帧时，可能遗漏对字幕生成至关重要的帧。 相反，提取过多的帧会包括连续的帧，可能导致从连续视频帧中提取的视觉标记出现冗余。 为了为每个视频提取适当数量的帧，本文提出了视频字幕中的第一个与模型无关的模块选择框架，该框架有两个主要功能：(1) 根据从视频帧中提取的视觉标记选择一个适当大小的字幕生成模块，以及(2) 为所选的字幕生成模块构建视觉标记的子集。 此外，我们提出了一种新的自适应注意力掩码方案，以增强对重要视觉标记的关注。 我们在三个不同的基准数据集上的实验表明，所提出的框架显著提高了三种最近视频字幕模型的性能。 显示英文摘要

摘要： Multi-modal transformers are rapidly gaining attention in video captioning tasks. Existing multi-modal video captioning methods typically extract a fixed number of frames, which raises critical challenges. When a limited number of frames are extracted, important frames with essential information for caption generation may be missed. Conversely, extracting an excessive number of frames includes consecutive frames, potentially causing redundancy in visual tokens extracted from consecutive video frames. To extract an appropriate number of frames for each video, this paper proposes the first model-agnostic module selection framework in video captioning that has two main functions: (1) selecting a caption generation module with an appropriate size based on visual tokens extracted from video frames, and (2) constructing subsets of visual tokens for the selected caption generation module. Furthermore, we propose a new adaptive attention masking scheme that enhances attention on important visual tokens. Our experiments on three different benchmark datasets demonstrate that the proposed framework significantly improves the performance of three recent video captioning models.