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Found 6 papers, 3 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.
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.
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 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.
Learning to attend in a brain-inspired deep neural network
Here we extend this work by building a more brain-inspired deep network model of the primate ATTention Network (ATTNet) that learns to shift its attention so as to maximize the reward.
Learned Region Sparsity and Diversity Also Predicts Visual Attention
Learned region sparsity has achieved state-of-the-art performance in classification tasks by exploiting and integrating a sparse set of local information into global decisions.
