David B Grimes | PubMed Health

PubMed2006

A probabilistic model of gaze imitation and shared attention.

An important component of language acquisition and cognitive learning is gaze imitation. Infants as young as one year of age can follow the gaze of an adult to determine the object the adult is focusing on. The ability to follow gaze is a precursor to shared attention, wherein two or more agents simultaneously focus their attention on a single object in the environment. Shared attention is a necessary skill for many complex, natural forms of learning, including learning based on imitation. This paper presents a probabilistic model of gaze imitation and shared attention that is inspired by Meltzoff and Moore's AIM model for imitation in infants. Our model combines a probabilistic algorithm for estimating gaze vectors with bottom-up saliency maps of visual scenes to produce maximum a posteriori (MAP) estimates of objects being looked at by an observed instructor. We test our model using a robotic system involving a pan-tilt camera head and show that combining saliency maps with gaze estimates leads to greater accuracy than using gaze alone. We additionally show that the system can learn instructor-specific probability distributions over objects, leading to increasing gaze accuracy over successive interactions with the instructor. Our results provide further support for probabilistic models of imitation and suggest new ways of implementing robotic systems that can interact with humans over an extended period of time.

Attention↗

PubMed2005

Bilinear sparse coding for invariant vision.

Recent algorithms for sparse coding and independent component analysis (ICA) have demonstrated how localized features can be learned from natural images. However, these approaches do not take image transformations into account. We describe an unsupervised algorithm for learning both localized features and their transformations directly from images using a sparse bilinear generative model. We show that from an arbitrary set of natural images, the algorithm produces oriented basis filters that can simultaneously represent features in an image and their transformations. The learned generative model can be used to translate features to different locations, thereby reducing the need to learn the same feature at multiple locations, a limitation of previous approaches to sparse coding and ICA. Our results suggest that by explicitly modeling the interaction between local image features and their transformations, the sparse bilinear approach can provide a basis for achieving transformation-invariant vision.

Algorithms↗