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Deep Visual-Semantic Alignments for Generating Image ...

Deep Visual-Semantic Alignments for Generating Image Descriptions Andrej Karpathy Li Fei-Fei Department of Computer Science, Stanford University Abstract We present a model that generates natural language de- scriptions of images and their regions. Our approach lever- ages datasets of images and their sentence descriptions to learn about the inter-modal correspondences between lan- guage and visual data. Our alignment model is based on a novel combination of Convolutional Neural Networks over Image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding.

Figure 2. Overview of our approach. A dataset of images and their sentence descriptions is the input to our model (left). Our model first infers the correspondences (middle, Section3.1) and then learns to generate novel descriptions (right, Section3.2).

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