@article {3827, title = {Finding any Waldo with zero-shot invariant and efficient visual search}, journal = {Nature Communications}, volume = {9}, year = {2018}, month = {09/2018}, abstract = {

Searching for a target object in a cluttered scene constitutes a fundamental challenge in daily vision. Visual search must be selective enough to discriminate the target from distractors, invariant to changes in the appearance of the target, efficient to avoid exhaustive exploration of the image, and must generalize to locate novel target objects with zero-shot training. Previous work on visual search has focused on searching for perfect matches of a target after extensive category-specific training. Here, we show for the first time that humans can efficiently and invariantly search for natural objects in complex scenes. To gain insight into the mechanisms that guide visual search, we propose a biologically inspired computational model that can locate targets without exhaustive sampling and which can generalize to novel objects. The model provides an approximation to the mechanisms integrating bottom-up and top-down signals during search in natural scenes.

}, doi = {10.1038/s41467-018-06217-x}, url = {http://www.nature.com/articles/s41467-018-06217-x}, author = {Zhang, Mengmi and Feng, Jiashi and Ma, Keng Teck and Lim, Joo Hwee and Qi Zhao and Gabriel Kreiman} } @article {3959, title = {What am I searching for?}, year = {2018}, month = {07/2018}, abstract = {

Can we infer intentions and goals from a person{\textquoteright}s actions? As an example of this family of problems, we consider here whether it is possible to decipher what a person is searching for by decoding their eye movement behavior. We conducted two human psychophysics experiments on object arrays and natural images where we monitored subjects{\textquoteright} eye movements while they were looking for a target object. Using as input the pattern of "error" fixations on non-target objects before the target was found, we developed a model (InferNet) whose goal was to infer what the target was. "Error" fixations share similar features with the sought target. The Infernet model uses a pre-trained 2D convolutional architecture to extract features from the error fixations and computes a 2D similarity map between the error fixation and all locations across the search image by modulating the search image via convolution across layers. InferNet consolidates the modulated response maps across layers via max pooling to keep track of the sub-patterns highly similar to features at error fixations and integrates these maps across all error fixations. InferNet successfully identifies the subject{\textquoteright}s goal and outperforms all the competitive null models, even without any object-specific training on the inference task.

}, author = {Zhang, Mengmi and Feng, Jiashi and Lim, Joo Hwee and Qi Zhao and Gabriel Kreiman} }