Summary of Exploring Simple Open-vocabulary Semantic Segmentation, by Zihang Lai
Exploring Simple Open-Vocabulary Semantic Segmentation
by Zihang Lai
First submitted to arxiv on: 22 Jan 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 The proposed model, S-Seg, enables semantic segmentation in images by assigning labels from arbitrary open-vocabulary texts to each pixel. Unlike existing approaches, S-Seg achieves strong performance without relying on visual-language (VL) models like CLIP, ground truth masks, or custom grouping encoders. Instead, it leverages pseudo-mask and language to train a MaskFormer using publicly available image-text datasets. This novel model directly trains for pixel-level features and language alignment, demonstrating excellent generalization capabilities across multiple testing datasets without requiring fine-tuning. Furthermore, S-Seg exhibits scalability with data and improves consistently when augmented with self-training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary S-Seg is a new way to match words with images. Imagine you have a picture of a cat, and you want to know what part of the image is the cat’s whiskers or ears. Existing methods use special machines (called visual-language models) that need lots of training data to work well. But S-Seg does something different. It uses a combination of fake masks and language to learn how to match words with images. This makes it easy to train using public data, and it works well on many different datasets without needing extra fine-tuning. S-Seg is also very good at getting better as it gets more training data. |
Keywords
* Artificial intelligence * Alignment * Fine tuning * Generalization * Mask * Self training * Semantic segmentation
