Question: What is most images’ captions on the Internet tend to do according to Micah Hodosh?
Evidence: In this section, we describes procedures of building our sportball Vietnamese dataset, called UIT-ViIC.
Our human resources for dataset construction involve five writers, whose ages are from 22-25. Being native Vietnamese residents, they are fluent in Vietnamese. All five UIT-ViIC creators first research and are trained about sports knowledge as well as the specialized vocabulary before starting to work.
During annotation process, there are inconsistencies and disagreements between human's understandings and the way they see images. According to Micah Hodosh et al BIBREF5, most images’ captions on Internet nowadays tend to introduce information that cannot be obtained from the image itself, such as people name, location name, time, etc. Therefore, to successfully compose meaningful descriptive captions we expect, their should be strict guidelines.
Inspired from MS-COCO annotation rules BIBREF16, we first sketched UIT-ViIC's guidelines for our captions:
Each caption must contain at least ten Vietnamese words.
Only describe visible activities and objects included in image.
Exclude name of places, streets (Chinatown, New York, etc.) and number (apartment numbers, specific time on TV, etc.)
Familiar English words such as laptop, TV, tennis, etc. are allowed.
Each caption must be a single sentence with continuous tense.
Personal opinion and emotion must be excluded while annotating.
Annotators can describe the activities and objects from different perspectives.
Visible “thing” objects are the only one to be described.
Ambiguous “stuff” objects which do not have obvious “border” are ignored.
In case of 10 to 15 objects which are in the same category or species, annotators do not need to include them in the caption.
In comparison with MS-COCO BIBREF16 data collection guidelines in terms of annotation, UIT-ViIC’s guidelines has similar rules (1, 2, 8, 9, 10) . We extend from MS-COCO’s guidelines with five new rules to our own and have modifications in the original ones.
In both datasets, we would like to control sentence length and focus on describing important subjects only in order to make sure that essential information is mainly included in captions. The MS-COCO threshold for sentence’s length is 8, and we raise the number to 10 for our dataset. One reason for this change is that an object in image is usually expressed in many Vietnamese words. For example, a “baseball player” in English can be translated into “vận động viên bóng chày” or “cầu thủ bóng chày”, which already accounted for a significant length of the Vietnamese sentence. In addition, captions must be single sentences with continuous tense as we expect our model’s output to capture what we are seeing in the image in a consise way.
On the other hand, proper name for places, streets, etc must not be mentioned in this dataset in order to avoid confusions and incorrect identification names with the same scenery for output. Besides, annotators’ personal opinion must be excluded for more meaningful captions. Vietnamese words for several English ones such as tennis, pizza, TV, etc are not existed, so annotators could use such familiar words in describing captions. For some images, the subjects are ambiguous and not descriptive which would be difficult for annotators to describe in words. That’s the reason why annotators can describe images from more than one perspective.
According to Micah Hodosh, most images’ captions on Internet nowadays tend to introduce information that cannot be obtained from the image itself, such as people name,location name, time, etc.
Answer: They tend to introduce information that cannot be obtained from the image itself, such as people name, location name, time, etc.