The Solution for the ICCV 2023 Perception Test Challenge 2023 -- Task 6 -- Grounded videoQA
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Abstract (arXiv)
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.
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References · 5
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
- 2021MDETR -- Modulated Detection for End-to-End Multi-Modal Understandingcited by 811
- 2023VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Datasetcited by 138
- 2022TubeDETR: Spatio-Temporal Video Grounding with Transformerscited by 97
- 2021Semi-Supervised Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transportcited by 28
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