2 citations · 2 across the 3 of their papers we have counts for
5 papers
Affinity-based Attention in Self-supervised Transformers Predicts Dynamics of Object Grouping in Humans
Hossein Adeli, Seoyoung Ahn, Nikolaus Kriegeskorte +1
The spreading of attention has been proposed as a mechanism for how humans group features to segment objects. However, such a mechanism has not yet been implemented and tested in n…
Reconstruction-guided attention improves the robustness and shape processing of neural networks
Seoyoung Ahn, Hossein Adeli, Gregory J. Zelinsky
Many visual phenomena suggest that humans use top-down generative or reconstructive processes to create visual percepts (e.g., imagery, object completion, pareidolia), but little i…
Recurrent Attention Models with Object-centric Capsule Representation for Multi-object Recognition
Hossein Adeli, Seoyoung Ahn, Gregory Zelinsky
The visual system processes a scene using a sequence of selective glimpses, each driven by spatial and object-based attention. These glimpses reflect what is relevant to the ongoin…
Predicting Goal-directed Attention Control Using Inverse-Reinforcement Learning
Gregory J. Zelinsky, Yupei Chen, Seoyoung Ahn +5
Understanding how goal states control behavior is a question ripe for interrogation by new methods from machine learning. These methods require large and labeled datasets to train…
Learning to attend in a brain-inspired deep neural network
Hossein Adeli, Gregory Zelinsky
Recent machine learning models have shown that including attention as a component results in improved model accuracy and interpretability, despite the concept of attention in these…