Summary of Enhancing Visual Question Answering Through Ranking-based Hybrid Training and Multimodal Fusion, by Peiyuan Chen and Zecheng Zhang and Yiping Dong and Li Zhou and Han Wang
Enhancing Visual Question Answering through Ranking-Based Hybrid Training and Multimodal Fusion
by Peiyuan Chen, Zecheng Zhang, Yiping Dong, Li Zhou, Han Wang
First submitted to arxiv on: 14 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); 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 Rank VQA model leverages a ranking-inspired hybrid training strategy to enhance Visual Question Answering (VQA) performance, integrating high-quality visual features from Faster R-CNN and rich semantic text features from BERT. The model fuses these features through multi-head self-attention mechanisms and incorporates a ranking learning module to optimize answer accuracy. The hybrid training strategy combines classification and ranking losses, enhancing generalization ability and robustness across diverse datasets. Experimental results demonstrate the effectiveness of Rank VQA, outperforming state-of-the-art models on VQA v2.0 and COCO-QA in terms of both accuracy and Mean Reciprocal Rank (MRR). The model’s superior performance is evident in its ability to handle complex questions requiring nuanced details and sophisticated inferences from image and text. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Rank VQA model helps computers better understand images and answer questions about them. It uses a special training method that combines visual features from the Faster R-CNN model with language features from BERT. This fusion allows the model to provide more accurate answers, especially for complex questions. The model is tested on two popular datasets and outperforms existing models in terms of accuracy and ranking. Overall, this research improves our ability to understand images and provides a foundation for further advancements in multimodal learning. |
Keywords
» Artificial intelligence » Bert » Classification » Cnn » Generalization » Question answering » Self attention
