While the ConvGRU approach shows promise in capturing local text features and enhancing performance in VQA tasks, there are potential limitations and drawbacks that need to be considered. One limitation could be related to the choice of kernel sizes in the convolutional layers. Using fixed kernel sizes may not always capture all relevant textual features optimally, especially in cases where the optimal kernel size varies across different types of questions. Future research could explore adaptive kernel sizes or dynamic convolutional strategies to address this limitation and adapt to the varying complexities of textual inputs.
Another drawback could be the computational complexity introduced by the convolutional layers, especially when dealing with large datasets or real-time applications. The additional processing required for convolutional feature extraction may impact the model's efficiency and scalability. Future research could focus on optimizing the ConvGRU architecture to reduce computational overhead while maintaining or improving performance. Techniques like parameter sharing, sparse convolutions, or efficient convolutional operations could be explored to address this challenge.
Additionally, the interpretability of the ConvGRU model may pose a challenge, as the impact of convolutional layers on the text representations may not always be transparent. Future research could investigate methods to enhance the interpretability of ConvGRU models, such as visualization techniques for understanding how different kernel sizes contribute to feature extraction and decision-making. By improving the transparency and interpretability of the model, researchers can gain deeper insights into the inner workings of ConvGRU and make more informed decisions about model design and optimization.