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Found 9 papers, 7 papers with code
Affinity-based Attention in Self-supervised Transformers Predicts Dynamics of Object Grouping in Humans
We found that our models of affinity spread that were built on feature maps from the self-supervised Transformers showed significant improvement over baseline and CNN based models on predicting reaction time patterns of humans, despite not being trained on the task or with any other object labels.
Gazeformer: Scalable, Effective and Fast Prediction of Goal-Directed Human Attention
In response, we pose a new task called ZeroGaze, a new variant of zero-shot learning where gaze is predicted for never-before-searched objects, and we develop a novel model, Gazeformer, to solve the ZeroGaze problem.
Unifying Top-down and Bottom-up Scanpath Prediction Using Transformers
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.
Reconstruction-guided attention improves the robustness and shape processing of neural networks
Ablation studies further reveal two complementary roles of spatial and feature-based attention in robust object recognition, with the former largely consistent with spatial masking benefits in the attention literature (the reconstruction serves as a mask) and the latter mainly contributing to the model's inference speed (i. e., number of time steps to reach a certain confidence threshold) by reducing the space of possible object hypotheses.
Target-absent Human Attention
In this paper, we propose the first data-driven computational model that addresses the search-termination problem and predicts the scanpath of search fixations made by people searching for targets that do not appear in images.
Recurrent Attention Models with Object-centric Capsule Representation for Multi-object Recognition
The visual system processes a scene using a sequence of selective glimpses, each driven by spatial and object-based attention.
Predicting Goal-directed Human Attention Using Inverse Reinforcement Learning
These maps were learned by IRL and then used to predict behavioral scanpaths for multiple target categories.
Predicting Goal-directed Attention Control Using Inverse-Reinforcement Learning
Using machine learning and the psychologically-meaningful principle of reward, it is possible to learn the visual features used in goal-directed attention control.
Effects of Linguistic Labels on Learned Visual Representations in Convolutional Neural Networks: Labels matter!
We investigated the changes in visual representations learnt by CNNs when using different linguistic labels (e. g., trained with basic-level labels only, superordinate-level only, or both at the same time) and how they compare to human behavior when asked to select which of three images is most different.
