36 citations · 45 across the 9 of their papers we have counts for
7 papers · 1 filter
Sequence and Circle: Exploring the Relationship Between Patches
Zhengyang Yu, Jochen Triesch
The vision transformer (ViT) has achieved state-of-the-art results in various vision tasks. It utilizes a learnable position embedding (PE) mechanism to encode the location of each…
Recurrent Feedback Improves Recognition of Partially Occluded Objects
Markus Roland Ernst, Jochen Triesch, Thomas Burwick
Recurrent connectivity in the visual cortex is believed to aid object recognition for challenging conditions such as occlusion. Here we investigate if and how artificial neural net…
Learning Hierarchical Integration of Foveal and Peripheral Vision for Vergence Control by Active Efficient Coding
Zhetuo Zhao, Jochen Triesch, Bertram E. Shi
The active efficient coding (AEC) framework parsimoniously explains the joint development of visual processing and eye movements, e.g., the emergence of binocular disparity selecti…
Self-Calibrating Active Binocular Vision via Active Efficient Coding with Deep Autoencoders
Charles Wilmot, Bertram E. Shi, Jochen Triesch
We present a model of the self-calibration of active binocular vision comprising the simultaneous learning of visual representations, vergence, and pursuit eye movements. The model…
Human-Expert-Level Brain Tumor Detection Using Deep Learning with Data Distillation and Augmentation
Diyuan Lu, Nenad Polomac, Iskra Gacheva +2
The application of Deep Learning (DL) for medical diagnosis is often hampered by two problems. First, the amount of training data may be scarce, as it is limited by the number of p…
Recurrent Connectivity Aids Recognition of Partly Occluded Objects
Markus Roland Ernst, Jochen Triesch, Thomas Burwick
Feedforward convolutional neural networks are the prevalent model of core object recognition. For challenging conditions, such as occlusion, neuroscientists believe that the recurr…