Summary of Shmamba: Structured Hyperbolic State Space Model For Audio-visual Question Answering, by Zhe Yang et al.
SHMamba: Structured Hyperbolic State Space Model for Audio-Visual Question Answering
by Zhe Yang, Wenrui Li, Guanghui Cheng
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Multimedia (cs.MM); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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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 proposed SHMamba model, a Structured Hyperbolic State Space Model, addresses limitations in traditional audio-visual question answering (AVQA) tasks by leveraging the benefits of hyperbolic geometry and state space models. By integrating hierarchical structures and complex relationships in audio-visual data, SHMamba outperforms previous methods with reduced parameters and computational costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The SHMamba model is designed to answer questions using both visual and audio inputs. It uses a special type of math called hyperbolic geometry to help the model understand complex patterns in the data. This helps it perform better than other models on similar tasks. The model also includes a way to adjust its “curvature” based on the data, which makes it more effective at capturing dynamic changes over time. |
Keywords
» Artificial intelligence » Question answering
