A computer-implemented method of improving accuracy in generating an answer to a question input related to an image input, the method comprising: receiving the image input; receiving the question input related to the image input; inputting the question input and the image input into an Attention-Based Configurable Convolutional Neural Networks, ABC-CNN, framework to generate an answer, the ABC-CNN framework comprising: an image feature map extraction component comprising a CNN that extracts an image feature map from the image input; a semantic question embedding component that obtains question embeddings from the question input; a question-guided attention map generation component that receives the image feature map and the question embeddings and that generates a question-guided attention map focusing on a region or regions asked by the question input; and an answer generation component that obtains an attention weighted image feature map by weighting image feature map using the question-guided attention map and generates answers based on a fusion of the image feature map, the question embeddings, and the attention weighted image feature map; wherein the question-guided attention map generation component comprises configurable convolutional kernels produced by projecting the question embeddings from a semantic space into a visual space and utilized to convolve with the image feature map to produce the question-guided attention map. receiving the image input; receiving the question input related to the image input; inputting the question input and the image input into an Attention-Based Configurable Convolutional Neural Networks, ABC-CNN, framework to generate an answer, the ABC-CNN framework comprising: an image feature map extraction component comprising a CNN that extracts an image feature map from the image input; a semantic question embedding component that obtains question embeddings from the question input; a question-guided attention map generation component that receives the image feature map and the question embeddings and that generates a question-guided attention map focusing on a region or regions asked by the question input; and an answer generation component that obtains an attention weighted image feature map by weighting image feature map using the question-guided attention map and generates answers based on a fusion of the image feature map, the question embeddings, and the attention weighted image feature map; wherein the question-guided attention map generation component comprises configurable convolutional kernels produced by projecting the question embeddings from a semantic space into a visual space and utilized to convolve with the image feature map to produce the question-guided attention map. an image feature map extraction component comprising a CNN that extracts an image feature map from the image input; a semantic question embedding component that obtains question embeddings from the question input; a question-guided attention map generation component that receives the image feature map and the question embeddings and that generates a question-guided attention map focusing on a region or regions asked by the question input; and an answer generation component that obtains an attention weighted image feature map by weighting image feature map using the question-guided attention map and generates answers based on a fusion of the image feature map, the question embeddings, and the attention weighted image feature map; wherein the question-guided attention map generation component comprises configurable convolutional kernels produced by projecting the question embeddings from a semantic space into a visual space and utilized to convolve with the image feature map to produce the question-guided attention map.