机器视觉课题组

模式识别国家重点实验室

中国科学院自动化研究所

  

最新科研进展

计算机视觉简介:历史、现状和发展趋势 [PDF]
胡占义

摘要:对计算机视觉40多年的发展历程进行了简要总结,包括:马尔计算视觉理论,主动视觉与目的视觉,多视几何与摄像机自标定,以及基于学习的视觉。在此基础上,对计算机视觉的未来发展趋势给出了一些展望。

  

生物视觉简介:图像物体识别通道 [PDF]
胡占义

摘要:对生物视觉系统,特别是用于物体识别的视觉腹部通道进行了简单介绍。另外,对群体神经元对物体的编码机制和不同皮层之间的反馈机理也进行了简介。由于读者的对象是从事计算机视觉的研究人员,所以,本章内容尽量简洁易懂,并尽量配以图示,以期对读者能提供一些帮助。

  
Statistics of Visual Responses to Object Stimuli from Primate AIT Neurons to DNN Neurons
Qiulei Dong, Hong Wang, Zhanyi Hu
Neural Computation 2017

Currently deep neural network (DNN) has achieved comparable image object categorization performance with human beings, however its exceptionally good categorization ability is not well understood. Recently, a goal-driven paradigm is proposed for the understanding of visual object recognition pathway [DiCarlo et al.2016], in which it is advocated that by only controlling the last layer’s categorization performance in the learning phase of a hierarchical liner-nonlinear networks, not only its last layer’s output can quantitatively predict IT neuron responses, but its intermediate layers can only automatically predict the responses of the intermediate visual areas, such as V4. In this work, we would explore whether the DNN neurons could possess similar image object representational statistics to monkey IT neurons, in particular, when the network becomes deeper, and the image category becomes larger, via VGG19, a typical deep network of 19 layers. Lehky et al.[2011,2014] systematically investigated the monkey’s IT neuron response statistics by three different measures: single neuron response selectivity, population response sparseness, and the intrinsic dimensionality of neural object representation. In this work, we used the above same three measures to evaluate the DNN neurons responses to images in ImageNet, which contains million images of 1000 different categories. Our results show that VGG19 neurons have quite different response statistics to image objects compared with IT neurons in [Lehky et al. 2011,2014], which seems indicate that a good hierarchical categorization network does not necessarily demand similar response statistics to images with the IT neurons.

 
Commentary: Using Goal-Driven Deep Learning Models to Understand Sensory Cortex
Qiulei Dong, Hong Wang, Zhanyi Hu
Frontiers in Computational Neuroscience 2018

Abstract: Recently, a goal-driven modeling approach of sensory cortex is proposed in (Yamins and DiCarlo, Nature Neuroscience 2016).The basic idea of this approach is to first optimize a hierarchical convolutional neural network (HCNN) for performing an ethologically relevant task, then once the network parameters have been fixed, to compare the outputs of different layers of the network to neural data. The success of this approach is exemplified by the results in (Yamins et al., PNAS 2014), where a 4-layer HCNN, called HMO, was used to predict IT neuron spikes on image object stimuli. In Hong et al. ( Nature Neuroscience 2016) , under the same approach, a 6-layer HCNN was trained on ImageNet to successfully predict category-orthogonal object properties along the ventral stream. In this commentary, we show that due to the inherent divergent feature learning phenomenon in HCNN learning, exposed by Li et al. (ICRL 2016), the goal-driven approach should be used with special care in sensory cortex understanding, in other words, its generality for modeling cortex should not be overestimated.