Summary of Llm-optic: Unveiling the Capabilities Of Large Language Models For Universal Visual Grounding, by Haoyu Zhao et al.
LLM-Optic: Unveiling the Capabilities of Large Language Models for Universal Visual Grounding
by Haoyu Zhao, Wenhang Ge, Ying-cong Chen
First submitted to arxiv on: 27 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 introduces LLM-Optic, a novel method that leverages Large Language Models (LLMs) to enhance visual grounding models in comprehending complex text queries. The proposed approach first uses an LLM as a Text Grounder to interpret the query and identify intended objects, then employs a pre-trained visual grounding model to generate candidate bounding boxes. Next, LLM-Optic annotates the candidates with numerical marks to establish connections between text and image regions. Finally, it uses a Large Multimodal Model (LMM) as a Visual Grounder to select marked candidates that best match the original query. This method achieves universal visual grounding without requiring additional training or fine-tuning, and demonstrates state-of-the-art zero-shot visual grounding capabilities on various challenging benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers understand what we mean when we point to something in an image. Currently, these computers struggle with complex sentences that involve multiple objects or spatial relationships. The new method, called LLM-Optic, uses special language models to figure out what we want to find in the image and then matches it to specific parts of the picture. This helps computers understand arbitrary text queries without needing more training. The results show that this approach is better than others at finding objects in images when given a description. |
Keywords
» Artificial intelligence » Fine tuning » Grounding » Zero shot
