  In this paper, we introduce a grounded video question-answering solution. Our
research reveals that the fixed official baseline method for video question
answering involves two main steps: visual grounding and object tracking.
However, a significant challenge emerges during the initial step, where
selected frames may lack clearly identifiable target objects. Furthermore,
single images cannot address questions like "Track the container from which the
person pours the first time." To tackle this issue, we propose an alternative
two-stage approach:(1) First, we leverage the VALOR model to answer questions
based on video information.(2) concatenate the answered questions with their
respective answers. Finally, we employ TubeDETR to generate bounding boxes for
the targets.
