Summary of Task Progressive Curriculum Learning For Robust Visual Question Answering, by Ahmed Akl et al.
Task Progressive Curriculum Learning for Robust Visual Question Answering
by Ahmed Akl, Abdelwahed Khamis, Zhe Wang, Ali Cheraghian, Sara Khalifa, Kewen Wang
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 In this paper, researchers present a novel approach to improve the robustness of Visual Question Answering (VQA) systems in out-of-distribution datasets. They propose Task Progressive Curriculum Learning (TPCL), which breaks down VQA problems into smaller, easier tasks based on question types and progressively trains models on a sequence of tasks. The method is conceptually simple, model-agnostic, and easy to implement. The authors demonstrate the effectiveness of TPCL through comprehensive evaluations on standard datasets, achieving state-of-the-art performance on VQA-CP v2, VQA-CP v1, and VQA v2 without using data augmentation or explicit debiasing mechanisms. TPCL also outperforms competitive robust VQA approaches by more than 5% and 7% on VQA-CP v2 and VQA-CP v1, respectively. Additionally, the authors show that TPCL can boost VQA baseline backbone performance by up to 28.5%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VQA systems are great at answering questions when they’re given specific types of data, but they struggle with new or unexpected information. Researchers have tried different ways to improve their performance, like combining answers from multiple models or adding more training data. In this study, scientists show that a simple and easy-to-use approach can actually do better than these complex methods. They call it Task Progressive Curriculum Learning (TPCL), and it works by breaking down the problem into smaller, easier questions. The method is simple to understand and implement, and it outperforms other approaches on standard datasets. |
Keywords
» Artificial intelligence » Curriculum learning » Data augmentation » Question answering
