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Found 6 papers, 1 papers with code
Diagnosing and exploiting the computational demands of videos games for deep reinforcement learning
Humans learn by interacting with their environments and perceiving the outcomes of their actions.
Stable and expressive recurrent vision models
We posit that the effectiveness of recurrent vision models is bottlenecked by the standard algorithm used for training them, "back-propagation through time" (BPTT), which has O(N) memory-complexity for training an N step model.
Robust pose tracking with a joint model of appearance and shape
We present a novel approach for estimating the 2D pose of an articulated object with an application to automated video analysis of small laboratory animals.
An Interval-Based Bayesian Generative Model for Human Complex Activity Recognition
We propose in this paper an atomic action-based Bayesian model that constructs Allen's interval relation networks to characterize complex activities with structural varieties in a probabilistic generative way: By introducing latent variables from the Chinese restaurant process, our approach is able to capture all possible styles of a particular complex activity as a unique set of distributions over atomic actions and relations.
Lie-X: Depth Image Based Articulated Object Pose Estimation, Tracking, and Action Recognition on Lie Groups
Pose estimation, tracking, and action recognition of articulated objects from depth images are important and challenging problems, which are normally considered separately.
Hand Action Detection from Ego-centric Depth Sequences with Error-correcting Hough Transform
Detecting hand actions from ego-centric depth sequences is a practically challenging problem, owing mostly to the complex and dexterous nature of hand articulations as well as non-stationary camera motion.
