Summary of Texim Fast: Text-to-image Representation For Semantic Similarity Evaluation Using Transformers, by Wazib Ansar et al.
TexIm FAST: Text-to-Image Representation for Semantic Similarity Evaluation using Transformers
by Wazib Ansar, Saptarsi Goswami, Amlan Chakrabarti
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 Text-to-Image methodology called TexIm FAST, which generates fixed-length representations through a self-supervised Variational Auto-Encoder (VAE) for semantic evaluation using transformers. The goal is to improve the informativeness of text representations while reducing memory footprint and complexity. TexIm FAST can handle variable-length sequences and reduces its parameters by 75%, making it more efficient for downstream tasks like Semantic Textual Similarity (STS). The authors tested TexIm FAST on several datasets, including MSRPC, CNN/Daily Mail, and XSum, achieving 6% improvement in accuracy compared to the baseline. This methodology is particularly useful for cross-modal applications such as text-to-image generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to convert text into images while keeping important details about language. The goal is to make computers understand text better and more efficiently. This new method, called TexIm FAST, uses special algorithms to reduce the amount of information needed to process text. It can handle texts of different lengths and improves how well it works for tasks like comparing two pieces of text. |
Keywords
» Artificial intelligence » Cnn » Encoder » Image generation » Self supervised
