20 citations · 27 across the 11 of their papers we have counts for
12 papers · 1 filter
Human-like Object Grouping in Self-supervised Vision Transformers
Hossein Adeli, Seoyoung Ahn, Andrew Luo +3
Vision foundation models trained with self-supervised objectives achieve strong performance across diverse tasks and exhibit emergent object segmentation properties. However, their…
Generating metamers of human scene understanding
Ritik Raina, Abe Leite, Alexandros Graikos +3
Human vision combines low-resolution "gist" information from the visual periphery with sparse but high-resolution information from fixated locations to construct a coherent underst…
Predicting Visual Attention in Graphic Design Documents
Souradeep Chakraborty, Zijun Wei, Conor Kelton +4
We present a model for predicting visual attention during the free viewing of graphic design documents. While existing works on this topic have aimed at predicting static saliency…
Look Hear: Gaze Prediction for Speech-directed Human Attention
Sounak Mondal, Seoyoung Ahn, Zhibo Yang +4
For computer systems to effectively interact with humans using spoken language, they need to understand how the words being generated affect the users' moment-by-moment attention.…
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…
Gazeformer: Scalable, Effective and Fast Prediction of Goal-Directed Human Attention
Sounak Mondal, Zhibo Yang, Seoyoung Ahn +3
Predicting human gaze is important in Human-Computer Interaction (HCI). However, to practically serve HCI applications, gaze prediction models must be scalable, fast, and accurate…
Unifying Top-down and Bottom-up Scanpath Prediction Using Transformers
Zhibo Yang, Sounak Mondal, Seoyoung Ahn +4
Most models of visual attention aim at predicting either top-down or bottom-up control, as studied using different visual search and free-viewing tasks. In this paper we propose th…
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…
Target-absent Human Attention
Zhibo Yang, Sounak Mondal, Seoyoung Ahn +3
The prediction of human gaze behavior is important for building human-computer interactive systems that can anticipate a user's attention. Computer vision models have been develope…
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 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…