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Found 11 papers, 2 papers with code
Why Not Use Your Textbook? Knowledge-Enhanced Procedure Planning of Instructional Videos
This knowledge, sourced from training procedure plans and structured as a directed weighted graph, equips the agent to better navigate the complexities of step sequencing and its potential variations.
A model for full local image interpretation
We describe a computational model of humans' ability to provide a detailed interpretation of components in a scene.
Emergent Neural Network Mechanisms for Generalization to Objects in Novel Orientations
The capability of Deep Neural Networks (DNNs) to recognize objects in orientations outside the distribution of the training data is not well understood.
What takes the brain so long: Object recognition at the level of minimal images develops for up to seconds of presentation time
Subjects were assigned to one of nine exposure conditions: 200, 500, 1000, 2000 ms with or without masking, as well as unlimited time.
Using Motion and Internal Supervision in Object Recognition
These two aspects are inter-related in the current study, since image motion is used for internal supervision, via the detection of spatiotemporal events of active-motion and the use of tracking.
Discovery and usage of joint attention in images
Second, we use a human study to demonstrate the sensitivity of humans to joint attention, suggesting that the detection of such a configuration in an image can be useful for understanding the image, including the goals of the agents and their joint activity, and therefore can contribute to image captioning and related tasks.
Large Field and High Resolution: Detecting Needle in Haystack
The growing use of convolutional neural networks (CNN) for a broad range of visual tasks, including tasks involving fine details, raises the problem of applying such networks to a large field of view, since the amount of computations increases significantly with the number of pixels.
Measuring and modeling the perception of natural and unconstrained gaze in humans and machines
How accurate are humans in determining the gaze direction of others in lifelike scenes, when they can move their heads and eyes freely, and what are the sources of information for the underlying perceptual processes?
Discovering containment: from infants to machines
Current artificial learning systems can recognize thousands of visual categories, or play Go at a champion"s level, but cannot explain infants learning, in particular the ability to learn complex concepts without guidance, in a specific order.
Do You See What I Mean? Visual Resolution of Linguistic Ambiguities
Understanding language goes hand in hand with the ability to integrate complex contextual information obtained via perception.
When Computer Vision Gazes at Cognition
(1) Human accuracy of discriminating targets 8{\deg}-10{\deg} of visual angle apart is around 40% in a free looking gaze task; (2) The ability to interpret gaze of different lookers vary dramatically; (3) This variance can be captured by the computational model; (4) Human outperforms the current model significantly.
