Using dropout in Visual Question Answering (VQA) is a common practice to prevent overfitting. However, the current way to use dropout in multi-path networks may cause two problems: the co-adaptations of neurons and the explosion of output variance. In this paper, we propose coherent dropout and siamese dropout mechanism to solve the two problems, respectively. Specifically, in coherent dropout, the relevant dropout layers in multiple paths are forced to work coherently to maximize the ability of preventing neuron co-adaptations. We show that the coherent dropout is simple in implementation but very effective to overcome overfitting. As for the explosion of output variance, we develop a siamese dropout mechanism to explicitly minimize the difference between the two output vectors produced from the same input data during training phase. Such mechanism can reduce the gap between training and inference phases and make the VQA model more robust. With the help of the two techniques, we further design an enhanced question encoder called Multi-path Stacked Residual RNNs which is deeper and wider and more powerful than current shallow question encoder. Extensive experiments are conducted to verify the effectiveness of coherent dropout, siamese dropout and the enhanced question encoder. And the results show that our methods can bring clear improvements to the state-of-the-art VQA models on VQA-vl and VQA-v2 datasets. (C) 2019 Elsevier Ltd. All rights reserved.