Summary of Vidctx: Context-aware Video Question Answering with Image Models, by Andreas Goulas et al.
VidCtx: Context-aware Video Question Answering with Image Models
by Andreas Goulas, Vasileios Mezaris, Ioannis Patras
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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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 The paper introduces VidCtx, a novel training-free framework for Video Question-Answering (VideoQA) tasks. The framework integrates visual information from input frames with textual descriptions of nearby frames to provide context. A pre-trained Large Multimodal Model (LMM) is used to extract question-aware textual descriptions (captions) at regular intervals and aggregate frame-level decisions using a max pooling mechanism. This approach enables the model to focus on relevant segments of the video, scaling to high numbers of frames. The paper compares VidCtx with approaches that rely on open models on three public VideoQA benchmarks: NExT-QA, IntentQA, and STAR. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to help computers answer questions about videos. Right now, computers can’t always understand what’s happening in a video because they don’t have enough information. The new method, called VidCtx, helps by combining visual information from the video with written descriptions of what’s happening at different points in time. This allows the computer to focus on the most important parts of the video and answer questions more accurately. The paper shows that this approach works well on three public tests of Video Question-Answering. |
Keywords
» Artificial intelligence » Question answering
