Detection and Captioning with Unseen Object Classes
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Image caption generation is one of the most challenging problems at the intersection of visual recognition and natural language modeling domains. In this work, we propose and study a practically important variant of this problem where test images may contain visual objects with no corresponding visual or textual training examples. For this problem, we propose a detection-driven approach based on a generalized zero-shot detection model and a template-based sentence generation model. In order to improve the detection component, we jointly define a class-to-class similarity based class representation and a practical score calibration mechanism. We also propose a novel evaluation metric that provides complimentary insights to the captioning outputs, by separately handling the visual and non-visual components of the captions. Our experiments show that the proposed zero-shot detection model obtains state-of-the-art performance on the MS-COCO dataset and the zero-shot captioning approach yields promising results.
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