Visual development and the acquisition of motion velocity sensitivities

Visual development and the acquisition of motion velocity sensitivities
复制标题

DOI:
10.1162/08997660360581895
复制
发表时间:
2003-04-01
期刊:
影响因子:
2.9
通讯作者:
Dominguez, M
Dominguez, M
中科院分区:
计算机科学4区
文献类型:
--
作者:
Jacobs, RA;Dominguez, M

文献摘要

被引文献

相似文献

我们认为,视觉感知的系统学习方面可能受益于在训练过程中使用适当设计的发展进程的假设。训练了四个模型来估计视觉图像序列中的运动速度。其中三个模型是发展模型,其视觉输入的性质在训练过程中发生了变化。这些模型在训练早期得到了相对贫乏的视觉输入,随着训练的进行,这种输入的质量有所提高。一个模型使用从粗略到多尺度的发展进程(它在训练早期获得粗略的运动特征,随着训练的进行,更精细的特征被添加到其输入中),另一个模型使用从精细到多尺度的进程,第三个模型使用随机进程。最终的模型是不发展的,因为其输入的性质在整个培训期间保持不变。仿真结果表明,从粗到多尺度的模型效果最好。为了解释该模型的优越性能,提出了一些假设,并给出了评估这些假设的仿真结果。我们的结论是,适当设计的发育序列对系统学习估计运动速度是有用的。视觉发展可以帮助视觉学习的想法是一个可行的假说,需要进一步研究。
We consider the hypothesis that systems learning aspects of visual perception may benefit from the use of suitably designed developmental progressions during training. Four models were trained to estimate motion velocities in sequences of visual images. Three of the models were developmental models in the sense that the nature of their visual input changed during the course of training. These models received a relatively impoverished visual input early in training, and the quality of this input improved as training progressed. One model used a coarse-to-multiscale developmental progression (it received coarse-scale motion features early in training and finer-scale features were added to its input as training progressed), another model used a fine-to-multiscale progression, and the third model used a random progression. The final model was nondevelopmental in the sense that the nature of its input remained the same throughout the training period. The simulation results show that the coarse-to-multiscale model performed best. Hypotheses are offered to account for this model's superior performance, and simulation results evaluating these hypotheses are reported. We conclude that suitably designed developmental sequences can be useful to systems learning to estimate motion velocities. The idea that visual development can aid visual learning is a viable hypothesis in need of further study.