| Summary: | An image contains a lot of information that humans can detect in a very short time. Image captioning aims to detect this information by describing the image content through image and text processing techniques. One of the peculiarities of the proposed approach is the combination of multiple networks to catch as many distinct features as possible from a semantic point of view. In this work, our goal is to prove that a combination strategy of existing methods can efficiently improve the performance in the object detection tasks concerning the performance achieved by each tested individually. This approach involves using different deep neural networks that perform two levels of hierarchical object detection in an image. The results are combined and used by a captioning module that generates image captions through natural language processing techniques. Several experimental results are reported and discussed to show the effectiveness of our framework. The combination strategy has also improved, showing a gain in precision over single models.
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