Summary of Causal Graphical Models For Vision-language Compositional Understanding, by Fiorenzo Parascandolo et al.
Causal Graphical Models for Vision-Language Compositional Understanding
by Fiorenzo Parascandolo, Nicholas Moratelli, Enver Sangineto, Lorenzo Baraldi, Rita Cucchiara
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multimedia (cs.MM)
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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 The proposed paper presents a novel approach to modeling dependency relations among textual and visual tokens in Vision-Language Models (VLMs). The authors identify that current VLMs struggle to fully understand the compositional properties of human language, which hinders their performance on tasks requiring mutual relationships between entities. To address this limitation, they introduce a Causal Graphical Model (CGM) built using dependency parsing and train a decoder conditioned by the VLM visual encoder. The CGM structure encourages the decoder to learn main causal dependencies in a sentence, discarding spurious correlations. Experimental results on five compositional benchmarks demonstrate significant improvements over state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows that computers can be better at understanding sentences if they think about how words are connected. Right now, computers just look at each word separately and don’t understand how the words work together. The authors want to fix this by teaching computers to see the relationships between words. They use a special kind of diagram called a graph to show how words are related. This helps the computer learn what’s important and what’s not. By doing this, the computer can become much better at understanding sentences and even do tasks that require it. |
Keywords
» Artificial intelligence » Decoder » Dependency parsing » Encoder
