Summary of Vlms Meet Uda: Boosting Transferability Of Open Vocabulary Segmentation with Unsupervised Domain Adaptation, by Roberto Alcover-couso et al.
VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation
by Roberto Alcover-Couso, Marcos Escudero-Viñolo, Juan C. SanMiguel, Jesus Bescos
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 method addresses limitations in segmentation models by adapting Vision-Language Models (VLMs) and leveraging synthetic data. The study explores two independent approaches to overcome categorical constraints during training. While VLMs can struggle with granularity, failing to disentangle fine-grained concepts, and synthetic data-based methods are limited by available datasets, this research aims to bridge the gap between these approaches and existing segmentation models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new method for segmentation models that allows them to go beyond the categories they were trained on. Two approaches are explored: using Vision-Language Models (VLMs) and creating synthetic data. VLMs can be tricky because they don’t do well with small details, but synthetic data only works as well as the fake data you create. The goal is to find a way to make these two methods work together better. |
Keywords
» Artificial intelligence » Synthetic data
