Summary of Tg-llava: Text Guided Llava Via Learnable Latent Embeddings, by Dawei Yan et al.
TG-LLaVA: Text Guided LLaVA via Learnable Latent Embeddings
by Dawei Yan, Pengcheng Li, Yang Li, Hao Chen, Qingguo Chen, Weihua Luo, Wei Dong, Qingsen Yan, Haokui Zhang, Chunhua Shen
First submitted to arxiv on: 15 Sep 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 This paper proposes a novel approach called Text Guided LLaVA (TG-LLaVA), which optimizes vision-language models (VLMs) by guiding the vision encoder with text. Building on the success of VLMs, researchers have primarily focused on improving the language model component, overlooking potential improvements to the vision encoder. TG-LLaVA uses learnable latent embeddings as a bridge to analyze textual instructions and refine the vision encoder. The method also extracts additional detailed information from local patches as auxiliary guidance. By incorporating text guidance, the vision encoder can extract text-related features, leading to better answer generation. Experiments on various datasets demonstrate the effectiveness of TG-LLaVA, which outperforms concurrent methods without requiring additional training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make computers better at understanding images and words together. Currently, computers are good at recognizing objects in pictures, but they can struggle when asked questions about those objects. The researchers came up with a solution called Text Guided LLaVA (TG-LLaVA), which helps the computer understand what’s important in an image by using text to guide its attention. This makes the computer better at answering questions and gives it more common sense, just like humans do when looking at images. The method is tested on different datasets and shown to be effective. |
Keywords
» Artificial intelligence » Attention » Encoder » Language model
