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Found 3 papers, 1 papers with code
HOIST-Former: Hand-held Objects Identification, Segmentation, and Tracking in the Wild
We address the challenging task of identifying, segmenting, and tracking hand-held objects, which is crucial for applications such as human action segmentation and performance evaluation.
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.
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