Summary of Open-vocabulary Panoptic Segmentation Using Bert Pre-training Of Vision-language Multiway Transformer Model, by Yi-chia Chen et al.
Open-Vocabulary Panoptic Segmentation Using BERT Pre-Training of Vision-Language Multiway Transformer Model
by Yi-Chia Chen, Wei-Hua Li, Chu-Song Chen
First submitted to arxiv on: 25 Dec 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 This research proposes a new method for open-vocabulary panoptic segmentation, which involves training models to generalize to an unlimited number of classes using limited categorized training data. The approach leverages the cross-modal attention between visual and linguistic features in BEiT-3, a large-scale vision-language pre-trained model. The proposed method, OMTSeg, uses another large-scale vision-language pre-trained model called BEiT-3 to achieve better performance. Experimental results show that OMTSeg outperforms state-of-the-art models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in computer vision called open-vocabulary panoptic segmentation. This means it can work with lots of different classes or categories without needing special training for each one. The researchers used a powerful AI model called BEiT-3 and its ability to combine visual and language features to create their new method, OMTSeg. They tested it and found that it works better than other state-of-the-art models. |
Keywords
* Artificial intelligence * Attention
