Summary of The Solution For the Iccv 2023 Perception Test Challenge 2023 — Task 6 — Grounded Videoqa, by Hailiang Zhang et al.
The Solution for the ICCV 2023 Perception Test Challenge 2023 – Task 6 – Grounded videoQA
by Hailiang Zhang, Dian Chao, Zhihao Guan, Yang Yang
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel grounded video question-answering solution that tackles challenges in visual grounding and object tracking. The traditional approach involves two steps: identifying target objects and tracking them across frames. However, this method is limited by its reliance on clearly identifiable objects, which may not always be present. To address this issue, the authors propose a two-stage approach combining the VALOR model for video-based question answering with TubeDETR for generating bounding boxes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a way to answer questions about videos. Right now, there’s a problem where we can’t always identify the objects in the video frames. The usual method involves looking at individual frames and trying to find specific objects. But this doesn’t work when we need to track objects over time, like “What is the container that someone pours from for the first time?” To solve this, the researchers came up with a new two-step approach: first, use a special model called VALOR to answer questions based on video information, and then use another model called TubeDETR to draw boxes around the targets. |
Keywords
» Artificial intelligence » Grounding » Object tracking » Question answering » Tracking
