Summary of Learning Trimodal Relation For Audio-visual Question Answering with Missing Modality, by Kyu Ri Park et al.
Learning Trimodal Relation for Audio-Visual Question Answering with Missing Modality
by Kyu Ri Park, Hong Joo Lee, Jung Uk Kim
First submitted to arxiv on: 23 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 framework ensures robust Audio-Visual Question Answering (AVQA) performance even when a modality is missing, which is crucial for real-world scenarios where device malfunctions and data transmission errors are common. The framework consists of two main components: a Relation-aware Missing Modal (RMM) generator that enhances the ability to recall missing modal information by understanding relationships among available modalities, and an Audio-Visual Relation-aware (AVR) diffusion model with Audio-Visual Enhancing (AVE) loss that leverages shared cues between audio-visual modalities. This method can provide accurate answers even when input modalities are incomplete or missing, making it applicable to various multi-modal scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in computers that help us understand what’s happening in videos and audio recordings. Right now, these systems get very confused if some of the audio or video is missing. For example, imagine watching a video where someone is speaking, but their words are cut off halfway through. The computer system would struggle to understand what they’re saying. This paper proposes a new way for computers to figure out the answer even when some of the information is missing. They use special algorithms that help them understand how audio and video relate to each other, which makes it easier for them to fill in the gaps. |
Keywords
* Artificial intelligence * Diffusion model * Multi modal * Question answering * Recall
