Performance Analysis of Traditional VQA Models Under Limited Computational Resources

TL;DR

This study evaluates traditional VQA models under limited computational resources, highlighting how simpler models like BidGRU can effectively perform, especially on numerical and counting questions, with insights into optimizing model parameters.

Contribution

It provides a comprehensive analysis of traditional VQA models under resource constraints, identifying optimal configurations and emphasizing the importance of attention and counting mechanisms.

Findings

BidGRU with 300 embedding dimension and 3000 vocabulary size performs best under constraints.

Attention mechanisms and counting information are crucial for complex reasoning tasks.

Simpler models can achieve competitive performance with proper tuning in resource-limited settings.

Abstract

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.