We introduce a new task, named video corpus visual answer localization
(VCVAL), which aims to locate the visual answer in a large collection of
untrimmed instructional videos using a natural language question. This task
requires a range of skills - the interaction between vision and language, video
retrieval, passage comprehension, and visual answer localization. In this
paper, we propose a cross-modal contrastive global-span (CCGS) method for the
VCVAL, jointly training the video corpus retrieval and visual answer
localization subtasks with the global-span matrix. We have reconstructed a
dataset named MedVidCQA, on which the VCVAL task is benchmarked. Experimental
results show that the proposed method outperforms other competitive methods
both in the video corpus retrieval and visual answer localization subtasks.
Most importantly, we perform detailed analyses on extensive experiments, paving
a new path for understanding the instructional videos, which ushers in further
research.

本论文介绍一项新的任务，即视频语料库视觉答案定位（VCVAL），旨在使用自然语言问题在大量未剪辑的指导视频中定位视觉答案，并提出了一种跨模态对比全跨度（CCGS）方法来解决 VCVAL 问题，并在 MedVidCQA 数据集上测试了该方法，结果表明该方法在视频语料库检索和视觉答案定位子任务中的表现优于其他竞争方法，可以帮助我们更好地理解指导视频的内容，为后续研究铺平了一条新的道路。