Question: What language of dataset do they use when evaluating state-of-the-art models?
Evidence: Generating descriptions for multimedia contents such as images and videos, so called Image Captioning, is helpful for e-commerce companies or news agencies. For instance, in e-commerce field, people will no longer need to put much effort into understanding and describing products' images on their websites because image contents can be recognized and descriptions are automatically generated. Inspired by Horus BIBREF0 , Image Captioning system can also be integrated into a wearable device, which is able to capture surrounding images and generate descriptions as sound in real time to guide people with visually impaired.
Image Captioning has attracted attentions from researchers in recent years BIBREF1, BIBREF2, BIBREF3, and there has been promising attempts dealing with language barrier in this task by extending existed dataset captions into different languages BIBREF3, BIBREF4.
In this study, generating image captions in Vietnamese language is put into consideration. One straightforward approach for this task is to translate English captions into Vietnamese by human or by using machine translation tool, Google translation. With the method of translating directly from English to Vietnamese, we found that the descriptions are sometimes confusing and unnatural to native people. Moreover, image understandings are cultural dependent, as in Western, people usually have different ways to grasp images and different vocabulary choices for describing contexts. For instance, in Fig. FIGREF2, one MS-COCO English caption introduce about "a baseball player in motion of pitching", which makes sense and capture accurately the main activity in the image. Though it sounds sensible in English, the sentence becomes less meaningful when we try to translate it into Vietnamese. One attempt of translating the sentence is performed by Google Translation, and the result is not as expected.


Therefore, we come up with the approach of constructing a Vietnamese Image Captioning dataset with descriptions written manually by human. Composed by Vietnamese people, the sentences would be more natural and friendlier to Vietnamese users. The main resources we used from MS-COCO for our dataset are images. Besides, we consider having our dataset focus on sportball category due to several reasons:
By concentrating on a specific domain we are more likely to improve performance of the Image Captioning models. We expect our dataset can be used to confirm or reject this hypothesis.
Sportball Image Captioning can be used in certain sport applications, such as supportting journalists describing great amount of images for their articles.
Our primary contributions of this paper are as follows:
Firstly, we introduce UIT-ViIC, the first Vietnamese dataset extending MS-COCO with manually written captions for Image Captioning. UIT-ViIC is published for research purposes.
Secondly, we introduce our annotation tool for dataset construction, which is also published to help annotators conveniently create captions.
Finally, we conduct experiments to evaluate state-of-the-art models (evaluated on English dataset) on UIT-ViIC dataset, then we analyze the performance results to have insights into our corpus.
The structure of the paper is organized as follows. Related documents and studies are presented in Section SECREF2. UIT-ViIC dataset creation is described in Section SECREF3. Section SECREF4 describes the methods we implement. The experimental results and analysis are presented in Section SECREF5. Conclusion and future work are deduced in Section SECREF6.
Finally, we conduct experiments to evaluate state-of-the-art models (evaluated on English dataset) on UIT-ViIC dataset, then we analyze the performance results to have insights into our corpus.
Answer: English dataset.