https://www.technologynetworks.com/neuroscience/news/humans-but-not-deep-neural-networks-often-miss-giant-targets-in-scenes-292665
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Size Matters in Visual Searches (原文有圖)
Before you read on, look for toothbrushes in the photo above. Find them? Both
of them? If you're like the vast majority of people, you honed in on the one
near the sink, but probably took a moment or two before seeing the other,
much larger one hanging on the wall. Although it is technically much more
visible and not out of context, for a while at least, your brain excluded
that enormous blue toothbrush in your visual search.
As it turns out, size matters. When we search through scenes for a particular
object, we often miss even giant targets when their size is inconsistent with
the rest of the scene. That's according to scientists at UC Santa Barbara,
where this curious phenomenon is being investigated in an effort to better
understand how humans conduct visual searches.
These new findings, by researchers in the Department of Psychological & Brain
Sciences, are published in the journal Current Biology.
"When something appears at the wrong scale, you will miss it more often
because your brain automatically ignores it," said UCSB professor Miguel
Eckstein, who specializes in computational human vision, visual attention and
search. Using scenes of ordinary objects where 14 targets were featured in
computer-generated images that varied in color, viewing angle and size, mixed
with "target-absent" scenes, the researchers asked 60 viewers to search for
these objects (e.g. toothbrush, parking meter, computer mouse) while
eye-tracking software monitored the paths of their gaze. They found that
people tended to miss the target more often when it was mis-scaled, even when
their gaze was directed to the incorrectly sized object.
Computer vision, by contrast, does not have this issue, the scientists
reported.
"The idea is when you first see a scene, your brain rapidly processes it
within a few hundred milliseconds or less, and then you use that information
to guide your search towards likely locations where the object typically
appears," Eckstein said. "Also, you focus your attention on objects that are
actually at the size that is consistent with the object that you're looking
for." The most advanced computer vision -- deep neural networks -- search
across entire scenes and use the visual properties of the object itself,
while humans also use the relationships between objects and their context
within the scene to guide their eyes.
This trend may seem like a deficiency on the part of humans, but the tables
were turned when human subjects and a deep neural network were asked to
verify the presence of different target objects in real-world scenes that may
or may not have them. In that round, the deep neural network reported a much
higher percentage of false positives. That is, they confirmed the presence
of, say, a cellphone in a scene where there were computer keyboards because
of their similarity in shape -- despite the fact that keyboards are several
times larger than a cellphone and, in the photo, are much larger than the
nearby hands that would be holding them.
"No human would do that," added former graduate Katie Koehler, now working at
Riot Games. "Just based on the size your brain would automatically discard
it." This mechanism, according to the researchers, is in fact a useful
strategy implemented by human brains to process scenes rapidly, eliminate
distractors and reduce false positives. While this blindness due to
inconsistent size may be an unwanted byproduct of the human brain's search
strategy, such scenarios are rare in the real world. With repeated exposure
to the unusual scenario, human observers will eventually adapt their visual
searches to accommodate it.
"The findings might suggest ways to improve computer vision by implementing
some of the tricks the brain utilizes to reduce false positives," said former
postdoctoral researcher Emre Akbas, now an assistant professor of computer
engineering at Middle East Technical University in Turkey, who was
responsible for the computer vision components of the project.
According to Eckstein, some people on the autism spectrum might not miss the
large targets in a scene. He is contemplating a study on that topic in the
future.
"There are some theories that suggest that people with autism spectrum
disorder focus more on local scene information and less on global structure,"
he said. "So there is a possibility that people with autism spectrum disorder
might miss the mis-scaled objects less often, but we won't know that until we
do the study."
In the more immediate future, research will look into the brain activity that
occurs when we view mis-scaled objects.
"Many studies have identified brain regions that process scenes and objects,
and now researchers are trying to understand which particular properties of
scenes and objects are represented in these regions," said postdoctoral
researcher Lauren Welbourne, whose current research concentrates on how
objects are represented in the cortex, and how scene context influences the
perception of objects. "And so what we're trying to do is find out how these
brain areas respond to objects that are either correctly or incorrectly
scaled within a scene. This may help us determine which regions are
responsible for making it more difficult for us to find objects if they are
mis-scaled."
This article has been republished from materials provided by UC Santa
Barbara. Note: material may have been edited for length and content. For
further information, please contact the cited source.
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