Summary of Boosting Audio Visual Question Answering Via Key Semantic-aware Cues, by Guangyao Li et al.
Boosting Audio Visual Question Answering via Key Semantic-Aware Cues
by Guangyao Li, Henghui Du, Di Hu
First submitted to arxiv on: 30 Jul 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 proposed Temporal-Spatial Perception Model (TSPM) aims to empower machine learning models to perceive key audio-visual cues related to questions in multimodal videos. The AVQA task involves answering questions about visual objects, sounds, and their interactions. To address this challenge, the TSPM proposes a framework that consists of temporal perception module, spatial perception module, and cross-modal interaction between audio and visual representations. The model uses declarative sentence prompts derived from question templates to identify critical segments relevant to the questions. Then, it merges visual tokens from selected segments to highlight key latent targets and integrates these cues to answer the question. Extensive experiments on multiple AVQA benchmarks demonstrate that the proposed framework excels in understanding audio-visual scenes and answering complex questions effectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Audio Visual Question Answering (AVQA) task is all about using videos to answer questions. This task is tricky because it involves many different things happening at once, like sounds and objects moving around each other. To make this work, the authors created a new model that can understand these complex video scenes and answer the questions correctly. The model uses special sentences to help it find the important parts of the videos and then combines those with information from the audio part of the video to get the right answer. The results show that this model is very good at doing this tricky task. |
Keywords
» Artificial intelligence » Machine learning » Question answering
