Summary of Unified Lexical Representation For Interpretable Visual-language Alignment, by Yifan Li et al.
Unified Lexical Representation for Interpretable Visual-Language Alignment
by Yifan Li, Yikai Wang, Yanwei Fu, Dongyu Ru, Zheng Zhang, Tong He
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper introduces a novel Visual-Language Alignment (VLA) framework, LexVLA, which learns a unified lexical representation for both visual and textual modalities without complex design. The authors use pre-trained uni-modal models, DINOv2 and Llama 2, as the building blocks. To address false discovery issues, an overuse penalty is proposed to prevent the lexical representation from frequently activating meaningless words. LexVLA outperforms baselines fine-tuned on larger datasets and those trained from scratch on even bigger datasets in cross-modal retrieval benchmarks. The authors conduct extensive experiments to analyze LexVLA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LexVLA is a new way of matching pictures with words. Usually, this process involves complex designs, but the researchers found a simpler approach using pre-trained models. They combined two models: DINOv2 for images and Llama 2 for text. To make sure the matches are accurate, they added a penalty to prevent the system from choosing wrong words too often. The results show that LexVLA works better than other methods on big datasets. |
Keywords
» Artificial intelligence » Alignment » Llama
