From simple innate biases to complex visual concepts

Abstract

Early in development, infants learn to solve visual problems that are highly challenging for current computational methods. We present a model that deals with two fundamental problems in which the gap between computational difficulty and infant learning is particularly striking: learning to recognize hands and learning to recognize gaze direction. The model is shown a stream of natural videos and learns without any supervision to detect human hands by appearance and by context, as well as direction of gaze, in complex natural scenes. The algorithm is guided by an empirically motivated innate mechanism-the detection of "mover" events in dynamic images, which are the events of a moving image region causing a stationary region to move or change after contact. Mover events provide an internal teaching signal, which is shown to be more effective than alternative cues and sufficient for the efficient acquisition of hand and gaze representations. The implications go beyond the specific tasks, by showing how domain-specific "proto concepts" can guide the system to acquire meaningful concepts, which are significant to the observer but statistically inconspicuous in the sensory input.A basic question in cognitive development is how we learn to understand the world on the basis of sensory perception and active exploration. Already in their first months of life, infants rapidly learn to recognize complex objects and events in their visual input (1-3). Probabilistic learning models, as well as connectionist and dynamical models, have been developed in recent years as powerful tools for extracting the unobserved causes of sensory signals (4-6). Some of these models can efficiently discover significant statistical regularities in the observed signals, which may be subtle and of high order, and use them to construct world models and guide behavior (7-10). However, even powerful statistical models have inherent difficulties with natural cognitive concepts, which depend not only on statistical regularities in the sensory input but also on their significance and meaning to the observer. For example, in learning to understand actions and goals, an important part is identifying the agents' hands, their configuration, and their interactions with objects (1-3). This is an example in which significant and meaningful features can be nonsalient and highly variable and therefore difficult to learn. Our testing shows that current computational methods for general object detection (11-13) applied to large training data do not result by themselves in automatically learning about hands. In contrast, detecting hands (14), paying attention to what they are doing (15, 16), and using them to make inferences and predictions (1-3, 17) are natural for humans and appear early in development. How is it possible for infants to acquire such concepts in early development?A large body of developmental studies has suggested that the human cognitive system is equipped through evolution with basic innate structures that facilitate the acquisition of meaningf...

Action as an innate bias for visual learning

“…the infant probably has no concept of objects at this stage (with a few probable exceptions such as faces), and the theory of Ullman et al (8) does not require it. Instead, they demonstrate that simple action events can be detected by local analysis of motion flow patterns.…”

“…In PNAS, Ullman et al (8) suggest a twist to this story by emphasizing the key role of motions that cause actions as innate biases that help bootstrap the learning of complex visual concepts. They address the phenomenon that infants are quick to learn models of hands and estimate the gaze direction of the owner of the hand.…”

“…Hands are associated with faces, to which infants are sensitive very early (as discussed later), but this is only indirect. The suggestion of Ullman et al (8) is that a key property of hands is their ability to perform actions.…”

“…In summary, Ullman et al (8) suggest that visual learning by infants is partially driven by simple action events that help the infant pick out interesting parts of the enormous set of visual stimuli and use them to bootstrap up to complex models. From this perspective, human fondness for action movies, particularly those that contain action events, may reflect a highly successful learning mechanism rather than a mindless search for cheap thrills.…”

Human concerned object detecting in video

“…In addition to this, computer can predict Observers Task (Kanan, et al, 2014), using a multi-fixation pattern analysis method. For saliency problem, computer need to analysis simple innate biases, and translate it to complex visual concepts (Ullman, et al, 2012). In this process, computer can learn a model to detect human hands and gaze direction.…”