Summary of Advancing Multi-modal Sensing Through Expandable Modality Alignment, by Shenghong Dai et al.
Advancing Multi-Modal Sensing Through Expandable Modality Alignment
by Shenghong Dai, Shiqi Jiang, Yifan Yang, Ting Cao, Mo Li, Suman Banerjee, Lili Qiu
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Signal Processing (eess.SP)
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 The paper introduces Babel, a scalable pre-trained multi-modal sensing neural network, which can align six sensing modalities: Wi-Fi, mmWave, IMU, LiDAR, video, and depth. Traditionally, multi-modality learning requires paired data from all modalities, but this is often scarce. To address this challenge, Babel transforms the N-modality alignment into a series of two-modality alignments using an expandable network architecture. This concept relies on novel techniques like pre-trained modality towers capitalizing on available single-modal networks and adaptive training strategies balancing contributions from new and established modalities. The proposed framework can be applied to various sensing applications, showcasing its potential in comprehending the physical world. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in sensing technology. Usually, when we want to use multiple types of sensors (like cameras, radar, and GPS) together, we need data from all of them at the same time. But often, this data is hard to find. To fix this, the authors created a new framework called Babel. It can combine data from six different sensor types: Wi-Fi, camera, motion sensor, laser range finder, video, and depth sensor. The key idea behind Babel is to break down the problem into smaller steps, using techniques that work well for two sensors at a time. This allows us to use more types of sensors together, which can help us better understand the physical world. |
Keywords
» Artificial intelligence » Alignment » Multi modal » Neural network
