Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  tackle image question ansring (ImageQA) problem by learning a convolutional neural network (CNN) with a dynamic parameter layer whose ights are determined adaptively based on questions. For the adaptive parameter prediction,  employ a separate parameter prediction network, which consists of gated recurrent unit (GRU) taking a question as its input and a fully-connected layer generating a set of candidate ights as its output. Hover, it is challenging to construct a parameter prediction network for a large number of parameters in the fully-connected dynamic parameter layer of the CNN.  reduce the complexity of this problem by incorporating a hashing technique, where the candidate ights given by the parameter prediction network are selected using a predefined hash function to determine individual ights in the dynamic parameter layer. The proposed network—joint network with the CNN for ImageQA and the parameter prediction network—is trained end-to-end through back-propagation, where its ights are initialized using a pre-trained CNN and GRU. The proposed algorithm illustrates the state-of-the-art performance on all available public ImageQA benchmarks.
['Hyeonwoo Noh', 'Paul Hongsuck Seo', 'Bohyung Han']
I am writing a research paper on a idea Image Question Answering Using Convolutional Neural Network with Dynamic Parameter Prediction
[{"from": "human", "value": "I am writing a research paper on a idea Image Question Answering Using Convolutional Neural Network with Dynamic Parameter Prediction"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  tackle image question ansring (ImageQA) problem by learning a convolutional neural network (CNN) with a dynamic parameter layer whose ights are determined adaptively based on questions. For the adaptive parameter prediction,  employ a separate parameter prediction network, which consists of gated recurrent unit (GRU) taking a question as its input and a fully-connected layer generating a set of candidate ights as its output. Hover, it is challenging to construct a parameter prediction network for a large number of parameters in the fully-connected dynamic parameter layer of the CNN.  reduce the complexity of this problem by incorporating a hashing technique, where the candidate ights given by the parameter prediction network are selected using a predefined hash function to determine individual ights in the dynamic parameter layer. The proposed network\u2014joint network with the CNN for ImageQA and the parameter prediction network\u2014is trained end-to-end through back-propagation, where its ights are initialized using a pre-trained CNN and GRU. The proposed algorithm illustrates the state-of-the-art performance on all available public ImageQA benchmarks."}]