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Recent progress on automatic generation of image captions has shown that itis possible to describe the most salient information conveyed by images withaccurate and meaningful sentences. In this paper, we propose an image captionsystem that exploits the parallel structures between images and sentences. Inour model, the process of generating the next word, given the previouslygenerated ones, is aligned with the visual perception experience where theattention shifting among the visual regions imposes a thread of visualordering. This alignment characterizes the flow of "abstract meaning", encodingwhat is semantically shared by both the visual scene and the text description.Our system also makes another novel modeling contribution by introducingscene-specific contexts that capture higher-level semantic information encodedin an image. The contexts adapt language models for word generation to specificscene types. We benchmark our system and contrast to published results onseveral popular datasets. We show that using either region-based attention orscene-specific contexts improves systems without those components. Furthermore,combining these two modeling ingredients attains the state-of-the-artperformance.

Aligning where to see and what to tell: image caption with region-based attention and scene factorization