在计算资源有限时，优化传统VQA模型的性能表现。
Performance Analysis of Traditional VQA Models Under Limited Computational Resources
- 采用双向GRU结合300维嵌入和3000词表，平衡精度与效率
- 该配置在数值与计数问题上表现最优，准确率超越其他模型
- 强调注意力机制与计数信息对复杂推理的关键作用
在计算资源受限的真实应用场景中，如何有效融合视觉与文本信息进行视觉问答（VQA）面临巨大挑战。本文研究传统模型在资源约束下的表现，重点提升数值与计数类问题的性能。我们评估了基于双向GRU（BidGRU）、GRU、双向LSTM（BidLSTM）和卷积神经网络（CNN）的模型，分析词汇量、微调策略及嵌入维度的影响。实验表明，嵌入维度为300、词汇量为3000的BidGRU模型在不增加计算开销的前提下达到最佳整体性能。消融实验凸显注意力机制与计数信息在资源受限环境下处理复杂推理任务的重要性。本研究为开发适用于低算力环境的高效VQA模型提供了重要参考。
原文摘要 · Abstract (English)
In real-world applications where computational resources are limited, effectively integrating visual and textual information for Visual Question Answering (VQA) presents significant challenges. This paper investigates the performance of traditional models under computational constraints, focusing on enhancing VQA performance, particularly for numerical and counting questions. We evaluate models based on Bidirectional GRU (BidGRU), GRU, Bidirectional LSTM (BidLSTM), and Convolutional Neural Networks (CNN), analyzing the impact of different vocabulary sizes, fine-tuning strategies, and embedding dimensions. Experimental results show that the BidGRU model with an embedding dimension of 300 and a vocabulary size of 3000 achieves the best overall performance without the computational overhead of larger models. Ablation studies emphasize the importance of attention mechanisms and counting information in handling complex reasoning tasks under resource limitations. Our research provides valuable insights for developing more efficient VQA models suitable for deployment in environments with limited computational capacity.
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