5 citations · 6 across the 4 of their papers we have counts for
5 papers · 1 filter
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…
A Study of Human Gaze Behavior During Visual Crowd Counting
Raji Annadi, Yupei Chen, Viresh Ranjan +3
In this paper, we describe our study on how humans allocate their attention during visual crowd counting. Using an eye tracker, we collect gaze behavior of human participants who a…
Predicting Goal-directed Human Attention Using Inverse Reinforcement Learning
Zhibo Yang, Lihan Huang, Yupei Chen +5
Being able to predict human gaze behavior has obvious importance for behavioral vision and for computer vision applications. Most models have mainly focused on predicting free-view…
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…