Summary of Knn-clip: Retrieval Enables Training-free Segmentation on Continually Expanding Large Vocabularies, by Zhongrui Gui et al.
kNN-CLIP: Retrieval Enables Training-Free Segmentation on Continually Expanding Large Vocabularies
by Zhongrui Gui, Shuyang Sun, Runjia Li, Jianhao Yuan, Zhaochong An, Karsten Roth, Ameya Prabhu, Philip Torr
First submitted to arxiv on: 15 Apr 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 presents a novel approach to open-vocabulary segmentation models that can adapt to continually growing vocabularies without requiring retraining or large memory costs. The authors introduce kNN-CLIP, a training-free strategy that uses a database of instance embeddings for semantic and panoptic segmentation. This method achieves zero forgetting, outperforming traditional continual training methods and even a zero-shot segmentation baseline. kNN-CLIP enables open-vocabulary segmentation methods to expand their vocabularies on any domain with a single pass through the data, while only storing compact embeddings. The approach minimizes both compute and memory costs, achieving state-of-the-art performance across large-vocabulary semantic and panoptic segmentation datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way for computers to understand and label images even when they’re using new vocabulary that wasn’t learned before. Right now, these models forget what they’ve learned when they see new words. The researchers came up with a new idea called kNN-CLIP that doesn’t need any extra training or storage space. It works by using a special kind of database that helps the model understand images better. This approach is really efficient and can even work on big datasets, making it a great step forward in this field. |
Keywords
» Artificial intelligence » Zero shot
