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Found 11 papers, 6 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.
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.
A Study of Human Gaze Behavior During Visual Crowd Counting
Analyzing the collected gaze behavior of ten human participants on thirty crowd images, we observe some common approaches for visual counting.
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.
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.
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.
Co-localization with Category-Consistent Features and Geodesic Distance Propagation
Co-localization is the problem of localizing objects of the same class using only the set of images that contain them.
Ranked #1 on
Object Localization
on PASCAL VOC 2012
Efficient Video Segmentation Using Parametric Graph Partitioning
Video segmentation is the task of grouping similar pixels in the spatio-temporal domain, and has become an important preprocessing step for subsequent video analysis.
