12. A computer-implemented method of generating an answer to a question related to an image, the method comprising steps of: extracting an image feature map from an input image comprising a plurality of pixels using a deep convolutional neural network; obtaining a dense question embedding from an input question related to the input image using a long short term memory (LSTM) layer; producing a plurality of question-configured kernels by projecting the dense question embedding from semantic space into visual space; convolving the question-configured kernels with the image feature map to generate a question-guided attention map; obtaining at a multi-class classifier an attention weighted image feature map by spatially weighting the image feature map using the question-guided attention map, the attention weighted feature map lowering weights of regions irrelevant to the question; and generating an answer to the question based on a fusion of the image feature map, the deep question embedding, and the attention weighted image feature map.
12. A computer-implemented method of generating an answer to a question related to an image, the method comprising steps of: extracting an image feature map from an input image comprising a plurality of pixels using a deep convolutional neural network; obtaining a dense question embedding from an input question related to the input image using a long short term memory (LSTM) layer; producing a plurality of question-configured kernels by projecting the dense question embedding from semantic space into visual space; convolving the question-configured kernels with the image feature map to generate a question-guided attention map; obtaining at a multi-class classifier an attention weighted image feature map by spatially weighting the image feature map using the question-guided attention map, the attention weighted feature map lowering weights of regions irrelevant to the question; and generating an answer to the question based on a fusion of the image feature map, the deep question embedding, and the attention weighted image feature map. 16. The method of claim 12 wherein the image feature map, the deep question embedding, and the attention weighted image feature map are fused by a nonlinear projection.