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Full-Text Articles in Life Sciences
Perceptual Grouping With Latent Noise, Ben Lonnqvist, Zhengqing Wu, Michael H. Herzog
Perceptual Grouping With Latent Noise, Ben Lonnqvist, Zhengqing Wu, Michael H. Herzog
MODVIS Workshop
Humans effortlessly group elements into objects and segment them from the background and other objects without supervision. For example, the black and white stripes of a zebra are grouped together despite vastly different colors. A thorough theoretical and empirical account of perceptual grouping is still missing – Deep Neural Networks (DNNs), which are considered leading models of the visual system still regularly fail at simplistic perceptual grouping tasks. Here, we propose a counterintuitive unsupervised computational approach to perceptual grouping and segmentation: that they arise because of neural noise, rather than in spite of it. We show that adding noise in …
Constraining The Binding Problem Using Maps, Zhixian Han, Anne Sereno
Constraining The Binding Problem Using Maps, Zhixian Han, Anne Sereno
MODVIS Workshop
We constrained the binding problem by creating maps of different attributes. We compared the performance of different models with different maps in our current study. Our preliminary results showed that the performance of the model is the highest when location maps were used. These results suggest that the optimal way to constrain the binding problem is to create location maps of different attributes.
Validity Of Neural Distance Measures In Representational Similarity Analysis, Fabian A. Soto, Emily R. Martin, Hyeonjeong Lee, Nafiz Ahmed, Juan Estepa, Kianoosh Hosseini, Olivia A. Stibolt, Valentina Roldan, Alycia Winters, Mohammadreza Bayat
Validity Of Neural Distance Measures In Representational Similarity Analysis, Fabian A. Soto, Emily R. Martin, Hyeonjeong Lee, Nafiz Ahmed, Juan Estepa, Kianoosh Hosseini, Olivia A. Stibolt, Valentina Roldan, Alycia Winters, Mohammadreza Bayat
MODVIS Workshop
No abstract provided.
Visual Expertise In An Anatomically-Inspired Model Of The Visual System, Garrison W. Cottrell, Martha Gahl, Shubham Kulkarni
Visual Expertise In An Anatomically-Inspired Model Of The Visual System, Garrison W. Cottrell, Martha Gahl, Shubham Kulkarni
MODVIS Workshop
We report on preliminary results of an anatomically-inspired deep learning model of the visual system and its role in explaining the face inversion effect. Contrary to the generally accepted wisdom, our hypothesis is that the face inversion effect can be accounted for by the representation in V1 combined with the reliance on the configuration of features due to face expertise. We take two features of the primate visual system into account: 1) The foveated retina; and 2) The log-polar mapping from retina to V1. We simulate acquisition of faces, etc., by gradually increasing the number of identities the network learns. …