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Found 8 papers, 3 papers with code
Semantic-Based Active Perception for Humanoid Visual Tasks with Foveal Sensors
To illustrate the benefits of using semantic information in scene exploration and visual search tasks, we compare its performance against traditional saliency-based models.
Wrist-Based Fall Detection: Towards Generalization across Datasets
Given this, the proposal of this research work is the creation of a new dataset that encompasses data from three different datasets, with more than 1300 fall samples and 28 K negative samples.
Learning to search for and detect objects in foveal images using deep learning
The human visual system processes images with varied degrees of resolution, with the fovea, a small portion of the retina, capturing the highest acuity region, which gradually declines toward the field of view's periphery.
3DSGrasp: 3D Shape-Completion for Robotic Grasp
However, in practice, PCDs are often incomplete when objects are viewed from few and sparse viewpoints before the grasping action, leading to the generation of wrong or inaccurate grasp poses.
Active Gaze Control for Foveal Scene Exploration
Active perception and foveal vision are the foundations of the human visual system.
Action-conditioned Benchmarking of Robotic Video Prediction Models: a Comparative Study
In contrast, we argue that if these systems are to be used to guide action, necessarily, the actions the robot performs should be encoded in the predicted frames.
Action Anticipation for Collaborative Environments: The Impact of Contextual Information and Uncertainty-Based Prediction
We propose to use the uncertainty about each prediction as an online decision-making criterion for action anticipation.
Applying Domain Randomization to Synthetic Data for Object Category Detection
Recent advances in deep learning-based object detection techniques have revolutionized their applicability in several fields.
