The phrase "attention mapping" is correct and usable in written English.
It can be used in contexts related to psychology, marketing, or data analysis, where the focus is on understanding how attention is distributed or allocated.
Example: "In our study, we employed attention mapping to analyze how participants focused on different elements of the visual stimuli presented."
Alternatives: "focus mapping" or "attention distribution".
We then propose novel models with different levels of explicit supervision for learning attention maps during training.
We evaluate attention maps generated by state-of-the-art VQA models against human attention both qualitatively (via visualizations) and quantitatively (via rank-order correlation).
We show on the popular Flickr30k and COCO datasets that introducing supervision of attention maps during training solidly improves both attention correctness and caption quality, showing the promise of making machine perception more human-like.
Specifically, we propose a quantitative evaluation metric for the consistency between the generated attention maps and human annotations, using recently released datasets with alignment between regions in images and entities in captions.
Then a couple, each in a class on one: 4) Andrew's allegorical sound-map, and 5) John's radio-static attention map, if map it could be called; perhaps the most explicit attempt (settings aside) to represent sound by another sound.
First of all, user-defined visual attention maps are adopted to analyze the probability distribution of the ROI.
However, if the visual attention map is available, it is also possible to substitute this map as the tracking efficiency [51].
First, slides of orienteering checkpoints were shown and afterwards participants had to answer questions concerning aspects of short-term memory, focus of attention, map interpretation, estimation or descriptive abilities.
A visual attention map is used to infer information about high-impact areas that are used to create stylized rendering with emphasis on the edges used in the composition of the final stylized image.
"It knows when your heartbeat or stress level rises and it records with a camera when your brain activity is highest to provide a sort of mood or attention map of your day".
