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Article summary:

1. This paper introduces a novel Dual-Level Collaborative Transformer (DLCT) network to combine the advantages of both region and grid features for image captioning.

2. The DLCT network includes a Dual-way Self Attention (DWSA) component to mine intrinsic properties, as well as a Comprehensive Relation Attention component to embed geometric information.

3. Experiments on the MS-COCO dataset show that the DLCT model achieves new state-of-the-art performance on both local and online test sets.

Article analysis:

The article is generally trustworthy and reliable, as it provides detailed information about the proposed DLCT model and its components, as well as results from experiments conducted on the MS-COCO dataset. The authors also provide a link to their code, which further adds to the trustworthiness of their work.

However, there are some potential biases in the article that should be noted. For example, the authors do not discuss any possible risks associated with using their model or any unexplored counterarguments that could be made against it. Additionally, they do not present both sides of an argument equally; instead they focus solely on promoting their own model and its advantages over existing models without exploring other options or perspectives.

In conclusion, while this article is generally trustworthy and reliable, there are some potential biases that should be taken into consideration when evaluating its content.