Summary of Multi-stage Knowledge Integration Of Vision-language Models For Continual Learning, by Hongsheng Zhang et al.
Multi-Stage Knowledge Integration of Vision-Language Models for Continual Learning
by Hongsheng Zhang, Zhong Ji, Jingren Liu, Yanwei Pang, Jungong Han
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 This paper proposes a novel approach to continually learning for Vision Language Models (VLMs), enabling them to effectively adapt to new data distributions without joint training. The authors recognize limitations in existing distillation-based methods, which fail to impart comprehensive knowledge and inadequately leverage multimodal information from the original training dataset. To address these challenges, they draw on Knowledge Integration Theory (KIT) and develop a Multi-Stage Knowledge Integration network (MulKI). MulKI comprises four stages: Eliciting Ideas, Adding New Ideas, Distinguishing Ideas, and Making Connections. By leveraging prototypes to align across modalities, the authors elicit cross-modal knowledge, add new knowledge through intra- and inter-modality relationships with prototypes, distinguish knowledge from two teacher models, and integrate preceding and new knowledge. This approach demonstrates significant improvements in maintaining zero-shot capabilities while supporting continual learning across diverse downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper helps computers learn better by using a new method called Multi-Stage Knowledge Integration (MulKI). Computers can already do things like recognize pictures without being trained on those specific pictures. However, they struggle to adapt when faced with new information or tasks. The authors of this paper want to improve how well computers can learn and remember new things. They propose a four-stage process that helps computers combine different types of knowledge from multiple sources. This approach shows promise in helping computers continue learning and adapting to new information without forgetting what they already know. |
Keywords
» Artificial intelligence » Continual learning » Distillation » Zero shot
