Summary of Foundation Models and Adaptive Feature Selection: a Synergistic Approach to Video Question Answering, by Sai Bhargav Rongali et al.
Foundation Models and Adaptive Feature Selection: A Synergistic Approach to Video Question Answering
by Sai Bhargav Rongali, Mohamad Hassan N C, Ankit Jha, Neha Bhargava, Saurabh Prasad, Biplab Banerjee
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 Medium Difficulty summary: This paper introduces Local-Global Question Aware Video Embedding (LGQAVE), a novel approach for video question-answering (VideoQA). Current methods struggle to integrate questions with video frames, leveraging semantic object-level abstractions. LGQAVE addresses this challenge by incorporating three innovations: cross-attention for frame sampling, miniGPT-based object graphs, and a question-aware dynamic graph transformer (Q-DGT) for refining local and global embeddings. The output is used to generate answers via a language model. Evaluations across multiple benchmarks demonstrate that LGQAVE outperforms existing models in delivering accurate multi-choice and open-ended answers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper solves a big problem with computers answering questions about videos. Right now, computers don’t do a great job of understanding what’s happening in the video when they’re trying to answer a question. The new approach, called LGQAVE, makes it better by looking at specific parts of the video that are important for the question and using special graphs to understand what’s going on. It also uses a language model to generate answers. In tests, this new method did much better than other methods in answering questions correctly. |
Keywords
» Artificial intelligence » Cross attention » Embedding » Language model » Question answering » Transformer
