Summary of Source-free Cross-modal Knowledge Transfer by Unleashing the Potential Of Task-irrelevant Data, By Jinjing Zhu et al.
Source-Free Cross-Modal Knowledge Transfer by Unleashing the Potential of Task-Irrelevant Data
by Jinjing Zhu, Yucheng Chen, Lin Wang
First submitted to arxiv on: 10 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 Source-free cross-modal knowledge transfer is a challenging task that aims to transfer knowledge between different modalities, such as RGB and depth or infrared, without access to the source data. Recent attempts have leveraged paired task-irrelevant (TI) data to match features and eliminate modality gaps. However, these approaches ignore the potential of TI data in estimating the source data distribution. This paper proposes a novel framework to unlock this potential by introducing two key technical components: Task-irrelevant Data-Guided Modality Bridging (TGMB) and Task-irrelevant Data-Guided Knowledge Transfer (TGKT). The TGMB module translates target modality data into source-like RGB images based on paired TI data, while the TGKT module transfers knowledge from the source model to the target model using self-supervised pseudo-labeling. Experimental results show that this method achieves state-of-the-art performance on three datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you want to teach a computer to understand pictures taken with different cameras or sensors. This is called cross-modal knowledge transfer, and it’s very hard when you don’t have any examples from the camera or sensor you’re trying to learn from. A recent idea was to use similar but not exactly the same images to help the computer learn. However, this idea didn’t fully use all the helpful information in those extra images. This paper suggests a new way to do this by first translating the different images into a format that’s easier for the computer to understand and then using that translated image to teach the computer what it needs to know. The results show that this method works really well on three different datasets. |
Keywords
* Artificial intelligence * Self supervised
