Summary of Towards Multimodal Open-set Domain Generalization and Adaptation Through Self-supervision, by Hao Dong et al.
Towards Multimodal Open-Set Domain Generalization and Adaptation through Self-supervision
by Hao Dong, Eleni Chatzi, Olga Fink
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Medium Difficulty summary: The paper addresses the task of open-set domain generalization (OSDG) in multimodal scenarios, where models recognize novel classes within unseen domains. Current works have only focused on unimodal OSDG within meta-learning frameworks, without considering multiple modalities as input. This work introduces a novel approach to Multimodal Open-Set Domain Generalization (MM-OSDG), utilizing self-supervision and proposing two innovative multimodal pretext tasks: Masked Cross-modal Translation and Multimodal Jigsaw Puzzles. These tasks facilitate the learning of multimodal representative features, enhancing generalization and open-class detection capabilities. Additionally, the paper proposes a novel entropy weighting mechanism to balance the loss across different modalities. The approach is extended to tackle Multimodal Open-Set Domain Adaptation (MM-OSDA) problems, where unlabeled data from the target domain is available. Experiments conducted on EPIC-Kitchens and HAC datasets demonstrate the efficacy and versatility of the proposed approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper solves a big problem in artificial intelligence called open-set domain generalization. It’s like teaching a machine to recognize new objects or actions it has never seen before, even if they are from a different source than what it was trained on. The current methods only work for one type of data at a time, but this paper introduces a new way to do it with multiple types of data together. This makes the machine more flexible and better at recognizing new things. The researchers also developed two new ways to train the machine to recognize patterns in different types of data. They tested their approach on several datasets and showed that it works well. |
Keywords
» Artificial intelligence » Domain adaptation » Domain generalization » Generalization » Meta learning » Translation
