Papers by Pascal Tilli
Beyond Accuracy: A Consolidated Tool for Visual Question Answering Benchmarking (2021.emnlp-demo)
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| Challenge: | Existing evaluation tools for general Visual Question Answering (VQA) systems are limited to answering accuracy, but they can be used to evaluate performance in real-world scenarios. |
| Approach: | They propose a browser-based benchmarking tool with an API for easy integration of new models and datasets to keep up with the fast-changing landscape of VQA. |
| Outcome: | The proposed tool tests generalization capabilities of models across multiple datasets and includes metrics that measure biases and uncertainty to further explain model behavior. |
Intrinsic Subgraph Generation for Interpretable Graph Based Visual Question Answering (2024.lrec-main)
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| Challenge: | Visual Question Answering (VQA) is acknowledged as a challenging multi-modal task for Machine Learning (ML). |
| Approach: | They propose an interpretable approach for graph-based Visual Question Answering . their model is designed to intrinsically produce a subgraph during the question-answering process as its explanation . |
| Outcome: | The proposed model outperforms existing explainable methods on a graph-based VQA dataset. |
Discrete Subgraph Sampling for Interpretable Graph based Visual Question Answering (2025.coling-main)
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| Challenge: | XAI aims to make machine learning models more transparent, but interpretable approaches are relatively rare. |
| Approach: | They integrate discrete subset sampling methods into a graph-based visual question answering system to evaluate their interpretability. |
| Outcome: | The proposed methods mitigate trade-off between interpretability and answer accuracy while achieving strong co-occurrences between answer and question tokens. |