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Searching for a target object in a cluttered scene constitutes a fundamentalchallenge in daily vision. Visual search must be selective enough todiscriminate the target from distractors, invariant to changes in theappearance of the target, efficient to avoid exhaustive exploration of theimage, and must generalize to locate novel target objects with zero-shottraining. Previous work has focused on searching for perfect matches of atarget after extensive category-specific training. Here we show for the firsttime that humans can efficiently and invariantly search for natural objects incomplex scenes. To gain insight into the mechanisms that guide visual search,we propose a biologically inspired computational model that can locate targetswithout exhaustive sampling and generalize to novel objects. The model providesan approximation to the mechanisms integrating bottom-up and top-down signalsduring search in natural scenes.

Finding any Waldo: zero-shot invariant and efficient visual search