Summary of Seeing Through Visualbert: a Causal Adventure on Memetic Landscapes, by Dibyanayan Bandyopadhyay et al.
Seeing Through VisualBERT: A Causal Adventure on Memetic Landscapes
by Dibyanayan Bandyopadhyay, Mohammed Hasanuzzaman, Asif Ekbal
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 The proposed framework for detecting offensive memes leverages a Structural Causal Model (SCM) to train VisualBERT in predicting the class of an input meme based on both meme input and causal concepts. This transparent interpretation enables understanding model behavior, determining whether misclassification is due to the right reason, and identifying reasons behind classification errors. The study qualitatively evaluates the framework’s effectiveness and quantitatively assesses the significance of proposed modelling choices, such as de-confounding, adversarial learning, and dynamic routing. Interestingly, input attribution methods do not guarantee causality within this framework, raising concerns about their reliability in safety-critical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to figure out why a computer is or isn’t correctly identifying memes as offensive. The current systems don’t show us how they make these decisions. Some methods try to explain their behavior by looking at what parts of the meme are most important, but this doesn’t always work well. To solve this problem, scientists propose a new approach that uses something called a Structural Causal Model (SCM). This method helps understand why the computer is or isn’t correctly identifying memes as offensive and can even tell us when it’s making mistakes because of bad reasons. The study shows how well this approach works and what it can do to help us make better computers. |
Keywords
* Artificial intelligence * Classification
