Compositional Reasoning refers to the ability to understand and manipulate complex structures or concepts by reasoning about their compositional parts and their interactions. In the context of image captioning, it involves the capacity to decompose an image into its constituent elements and reason about how these elements interact to form a coherent description.

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Retrieval, Analogy, and Composition: A framework for Compositional Generalization in Image Captioning

Retrieval, Analogy, and Composition: A framework for Compositional Generalization in Image Captioning Image captioning systems are expected to have the ability to combine individual concepts when describing scenes with concept combinations that are not observed during training. In spite of significant progress in image captioning with the help of the autoregressive generation framework, current approaches fail to generalize well to novel concept combinations. We propose a new framework that revolves around probing several similar image caption training instances (retrieval), performing analogical reasoning over relevant entities in retrieved prototypes (analogy), and enhancing the generation process with reasoning outcomes (composition). Our method augments the generation model by referring to the neighboring instances in the training set to produce novel concept combinations in generated captions. We perform experiments on the widely used image captioning benchmarks. The proposed models achieve substantial improvement over the compared baselines on both composition-related evaluation metrics and conventional image captioning metrics.