Summary of Beyond Sight: Towards Cognitive Alignment in Lvlm Via Enriched Visual Knowledge, by Yaqi Zhao and Yuanyang Yin and Lin Li and Mingan Lin and Victor Shea-jay Huang and Siwei Chen and Weipeng Chen and Baoqun Yin and Zenan Zhou and Wentao Zhang
Beyond Sight: Towards Cognitive Alignment in LVLM via Enriched Visual Knowledge
by Yaqi Zhao, Yuanyang Yin, Lin Li, Mingan Lin, Victor Shea-Jay Huang, Siwei Chen, Weipeng Chen, Baoqun Yin, Zenan Zhou, Wentao Zhang
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 A novel investigation into the limitations of Large Vision-Language Models (LVLMs) reveals that a mismatch between the vision encoder’s representation of visual information and the large language model’s cognitive framework hampers comprehension. This “cognitive misalignment” arises from the disparity between the visual features and the language model’s interpretive range, leading to reduced accuracy when encountering unknown or ambiguous data. A multi-granularity landmark dataset is constructed to examine the impact of varying vision encoder representations on LVLM performance. The results show that unknown data limits comprehension, while known data rich in distinctive features reduces cognitive misalignment. To address this limitation, a method called Entity-Enhanced Cognitive Alignment (EECA) is proposed, which generates visually enriched tokens that align with both the embedding space and the language model’s cognitive framework, significantly enhancing LVLM performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Vision-Language Models are super smart computers that can understand pictures and words. But sometimes, they get confused because the way they think about pictures doesn’t match how they process words. This problem is called “cognitive misalignment.” To solve this, researchers created a special dataset of landmarks with different levels of detail. They found that when the computer sees new or unclear images, it gets even worse at understanding them. But if it’s shown more detailed and clear images, its accuracy improves. The team developed a method to help computers better understand pictures and words by creating special tokens that match how they process information. This helps Large Vision-Language Models do their job much better. |
Keywords
» Artificial intelligence » Alignment » Embedding space » Encoder » Language model » Large language model
