@article {3764, title = {Recurrent computations for visual pattern completion}, journal = {Proceedings of the National Academy of Sciences}, year = {2018}, month = {08/2018}, abstract = {

Making inferences from partial information constitutes a critical aspect of cognition. During visual perception, pattern completion enables recognition of poorly visible or occluded objects. We combined psychophysics, physiology, and computational models to test the hypothesis that pattern completion is implemented by recurrent computations and present three pieces of evidence that are consistent with this hypothesis. First, subjects robustly recognized objects even when they were rendered \<15\% visible, but recognition was largely impaired when processing was interrupted by backward masking. Second, invasive physiological responses along the human ventral cortex exhibited visually selective responses to partially visible objects that were delayed compared with whole objects, suggesting the need for additional computations. These physiological delays were correlated with the effects of backward masking. Third, state-of-the-art feed-forward computational architectures were not robust to partial visibility. However, recognition performance was recovered when the model was augmented with attractor-based recurrent connectivity. The recurrent model was able to predict which images of heavily occluded objects were easier or harder for humans to recognize, could capture the effect of introducing a backward mask on recognition behavior, and was consistent with the physiological delays along the human ventral visual stream. These results provide a strong argument of plausibility for the role of recurrent computations in making visual inferences from partial information.

}, keywords = {Artificial Intelligence, computational neuroscience, Machine Learning, pattern completion, Visual object recognition}, issn = {0027-8424}, doi = {10.1073/pnas.1719397115}, url = {http://www.pnas.org/lookup/doi/10.1073/pnas.1719397115}, author = {Hanlin Tang and Martin Schrimpf and William Lotter and Moerman, Charlotte and Paredes, Ana and Ortega Caro, Josue and Hardesty, Walter and David Cox and Gabriel Kreiman} } @article {2408, title = {Full interpretation of minimal images}, year = {2017}, month = {02/2017}, abstract = {

The goal in this work is to model the process of {\textquoteleft}full interpretation{\textquoteright} of object images, which is the ability to identify and localize all semantic features and parts that are recognized by human observers. The task is approached by dividing the interpretation of the complete object to the interpretation of multiple reduced but interpretable local regions. In such reduced regions, interpretation is simpler, since the number of semantic components is small, and the variability of possible configurations is low.

We model the interpretation process by identifying primitive components and relations that play a useful role in local interpretation by humans. To identify useful components and relations used in the interpretation process, we consider the interpretation of {\textquoteleft}minimal configurations{\textquoteright}: these are reduced local regions, which are minimal in the sense that further reduction renders them unrecognizable and uninterpretable. We show that such minimal interpretable images have useful properties, which we use to identify informative features and relations used for full interpretation. We describe our interpretation model, and show results of detailed interpretations of minimal configurations, produced automatically by the model. Finally, we discuss implications of full interpretation to difficult visual tasks, such as recognizing human activities or interactions, which are beyond the scope of current models of visual recognition.

This manuscript has beed accepted for publication in Cognition.

}, keywords = {Image interpretation, Parts and relations, Visual object recognition}, author = {Guy Ben-Yosef and Liav Assif and Shimon Ullman} }