Summary of Olive: Object Level In-context Visual Embeddings, by Timothy Ossowski et al.
OLIVE: Object Level In-Context Visual Embeddings
by Timothy Ossowski, Junjie Hu
First submitted to arxiv on: 2 Jun 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 novel method prompts large language models with in-context visual object vectors, enabling controllable object-level reasoning. This eliminates the need for fusing lengthy image patch features and speeds up training. The model achieves competitive referring object classification and captioning performance while offering zero-shot generalization and robustness to visually challenging contexts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Recent advances in vision-language models (VLMs) have shown impressive abilities across various tasks. However, these models still struggle with understanding objects at a fine level and grounding them. To address this, a new method is proposed that uses object vectors to prompt large language models, allowing for controlled object-level thinking. This approach eliminates the need for combining many image patch features and speeds up training. |
Keywords
» Artificial intelligence » Classification » Generalization » Grounding » Prompt » Zero shot
