  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.
