Summary of An Efficient and Streaming Audio Visual Active Speaker Detection System, by Arnav Kundu et al.
An Efficient and Streaming Audio Visual Active Speaker Detection System
by Arnav Kundu, Yanzi Jin, Mohammad Sekhavat, Max Horton, Danny Tormoen, Devang Naik
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the challenge of Active Speaker Detection (ASD) in real-time video frames, where models must determine whether a person is speaking or not. Current approaches excel in network architecture and representation learning but struggle with latency and memory usage, making them impractical for immediate applications. To address this gap, the authors propose two scenarios: limiting future context frames to reduce latency and constraining past frames to mitigate memory issues. The proposed constrained transformer models achieve performance comparable to or better than state-of-the-art recurrent models like uni-directional GRUs, with reduced context frames. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers figure out when someone is speaking in a video. Right now, computers are good at learning patterns in speech but not great at doing it quickly and efficiently. The authors suggest two ways to make the computer do this faster: reduce how many future frames it looks at and limit how many old frames it remembers. The new method works almost as well as older methods that use more memory and processing power. |
Keywords
» Artificial intelligence » Representation learning » Transformer
