Summary of Bridging Vision and Language Spaces with Assignment Prediction, by Jungin Park and Jiyoung Lee and Kwanghoon Sohn
Bridging Vision and Language Spaces with Assignment Prediction
by Jungin Park, Jiyoung Lee, Kwanghoon Sohn
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 introduces VLAP, a novel approach that bridges pre-trained vision models and large language models (LLMs) to enable frozen LLMs to understand visual data. The method transforms the embedding space of pre-trained vision models into the LLM’s word embedding space using a single linear layer for efficient and general-purpose visual and language understanding. The paper formulates the assignment procedure as an optimal transport problem, assigning multimodal data to a set of word embeddings within pre-trained LLMs. This allows vision and language representations to contain the same information, grounding frozen LLMs’ word embedding space in visual data. Experimental results show that VLAP achieves substantial improvements over previous linear transformation-based approaches across various vision-language tasks, including image captioning, visual question answering, and cross-modal retrieval. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to connect computer vision (looking at pictures) and natural language processing (understanding words). It’s called VLAP. This approach helps machines understand pictures better by using information from both areas. The idea is to make frozen language models (machines that can process text) understand what they see in pictures. This is important because it allows these machines to perform tasks like writing captions for pictures or answering questions about what’s happening in a picture. The paper shows that this approach works well and improves the performance of machines on various tasks. |
Keywords
» Artificial intelligence » Embedding space » Grounding » Image captioning » Language understanding » Natural language processing » Question answering
