Wave Propagation of Visual Stimuli in Focus of Attention
- URL: http://arxiv.org/abs/2006.11035v1
- Date: Fri, 19 Jun 2020 09:33:21 GMT
- Title: Wave Propagation of Visual Stimuli in Focus of Attention
- Authors: Lapo Faggi, Alessandro Betti, Dario Zanca, Stefano Melacci, Marco Gori
- Abstract summary: Fast reactions to changes in the surrounding visual environment require efficient attention mechanisms to reallocate computational resources to most relevant locations in the visual field.
We present a biologically-plausible model of focus of attention that exhibits effectiveness and efficiency exhibited by foveated animals.
- Score: 77.4747032928547
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Fast reactions to changes in the surrounding visual environment require
efficient attention mechanisms to reallocate computational resources to most
relevant locations in the visual field. While current computational models keep
improving their predictive ability thanks to the increasing availability of
data, they still struggle approximating the effectiveness and efficiency
exhibited by foveated animals. In this paper, we present a
biologically-plausible computational model of focus of attention that exhibits
spatiotemporal locality and that is very well-suited for parallel and
distributed implementations. Attention emerges as a wave propagation process
originated by visual stimuli corresponding to details and motion information.
The resulting field obeys the principle of "inhibition of return" so as not to
get stuck in potential holes. An accurate experimentation of the model shows
that it achieves top level performance in scanpath prediction tasks. This can
easily be understood at the light of a theoretical result that we establish in
the paper, where we prove that as the velocity of wave propagation goes to
infinity, the proposed model reduces to recently proposed state of the art
gravitational models of focus of attention.
Related papers
- TPP-Gaze: Modelling Gaze Dynamics in Space and Time with Neural Temporal Point Processes [63.95928298690001]
We present TPP-Gaze, a novel and principled approach to model scanpath dynamics based on Neural Temporal Point Process (TPP)
Our results show the overall superior performance of the proposed model compared to state-of-the-art approaches.
arXiv Detail & Related papers (2024-10-30T19:22:38Z) - Injecting Hamiltonian Architectural Bias into Deep Graph Networks for Long-Range Propagation [55.227976642410766]
dynamics of information diffusion within graphs is a critical open issue that heavily influences graph representation learning.
Motivated by this, we introduce (port-)Hamiltonian Deep Graph Networks.
We reconcile under a single theoretical and practical framework both non-dissipative long-range propagation and non-conservative behaviors.
arXiv Detail & Related papers (2024-05-27T13:36:50Z) - WISE: full-Waveform variational Inference via Subsurface Extensions [1.4747234049753455]
We introduce a probabilistic technique for full-waveform inversion, employing variational inference and conditional normalizing flows.
Our approach integrates generative artificial intelligence with physics-informed common-image gathers, reducing reliance on accurate initial velocity models.
arXiv Detail & Related papers (2023-12-11T00:58:33Z) - Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep
Neural Sequence Models [0.7829352305480283]
In this work, we employ established gaze event detection algorithms for fixations and saccades.
We quantitatively evaluate the impact of these events by determining their concept influence.
arXiv Detail & Related papers (2023-04-12T10:15:31Z) - Hybrid Predictive Coding: Inferring, Fast and Slow [62.997667081978825]
We propose a hybrid predictive coding network that combines both iterative and amortized inference in a principled manner.
We demonstrate that our model is inherently sensitive to its uncertainty and adaptively balances balances to obtain accurate beliefs using minimum computational expense.
arXiv Detail & Related papers (2022-04-05T12:52:45Z) - Gravitational Models Explain Shifts on Human Visual Attention [80.76475913429357]
Visual attention refers to the human brain's ability to select relevant sensory information for preferential processing.
Various methods to estimate saliency have been proposed in the last three decades.
We propose a gravitational model (GRAV) to describe the attentional shifts.
arXiv Detail & Related papers (2020-09-15T10:12:41Z) - Focus of Attention Improves Information Transfer in Visual Features [80.22965663534556]
This paper focuses on unsupervised learning for transferring visual information in a truly online setting.
The computation of the entropy terms is carried out by a temporal process which yields online estimation of the entropy terms.
In order to better structure the input probability distribution, we use a human-like focus of attention model.
arXiv Detail & Related papers (2020-06-16T15:07:25Z) - Toward Improving the Evaluation of Visual Attention Models: a
Crowdsourcing Approach [21.81407627962409]
State-of-the-art models focus on learning saliency maps from human data.
We highlight the limits of the current metrics for saliency prediction and scanpath similarity.
We present a study aimed at evaluating how strongly the scanpaths generated with the unsupervised gravitational models appear plausible to naive and expert human observers.
arXiv Detail & Related papers (2020-02-11T14:27:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.