Young children develop sophisticated internal models of the world based on
their egocentric visual experience. How much of this is driven by innate
constraints and how much is driven by their experience? To investigate these
questions, we train state-of-the-art neural networks on a realistic proxy of a
child's visual experience without any explicit supervision or domain-specific
inductive biases. Specifically, we train both embedding models and generative
models on 200 hours of headcam video from a single child collected over two
years. We train a total of 72 different models, exploring a range of model
architectures and self-supervised learning algorithms, and comprehensively
evaluate their performance in downstream tasks. The best embedding models
perform at 70% of a highly performant ImageNet-trained model on average. They
also learn broad semantic categories without any labeled examples and learn to
localize semantic categories in an image without any location supervision.
However, these models are less object-centric and more background-sensitive
than comparable ImageNet-trained models. Generative models trained with the
same data successfully extrapolate simple properties of partially masked
objects, such as their texture, color, orientation, and rough outline, but
struggle with finer object details. We replicate our experiments with two other
children and find very similar results. Broadly useful high-level visual
representations are thus robustly learnable from a representative sample of a
child's visual experience without strong inductive biases.

通过训练神经网络从一个孩子的视觉经验中学习，我们研究了基于本能的约束和基于经验的影响对于孩子内部模型的形成的影响，并发现在没有强烈的归纳偏见的情况下，孩子的代表性样本足以从其中获得广泛有用的高级视觉表示。

通用神经网络能从儿童的视觉经验中学到什么？

What can generic neural networks learn from a child's visual experience?

Processes occurring in brains, a.k.a. biological neural networks, can and
have been modeled within artificial neural network architectures. Due to this,
we have conducted a review of research on the phenomenon of blindsight in an
attempt to generate ideas for artificial intelligence models. Blindsight can be
considered as a diminished form of visual experience. If we assume that
artificial networks have no form of visual experience, then deficits caused by
blindsight give us insights into the processes occurring within visual
experience that we can incorporate into artificial neural networks. This
article has been structured into three parts. Section 2 is a review of
blindsight research, looking specifically at the errors occurring during this
condition compared to normal vision. Section 3 identifies overall patterns from
Section 2 to generate insights for computational models of vision. Section 4
demonstrates the utility of examining biological research to inform artificial
intelligence research by examining computation models of visual attention
relevant to one of the insights generated in Section 3. The research covered in
Section 4 shows that incorporating one of our insights into computational
vision does benefit those models. Future research will be required to determine
whether our other insights are as valuable.

通过对盲视现象的研究和分析，本文探讨了生物神经网络和人工神经网络的相似之处，提取出了其中一些重要的洞见用于计算机视觉中。

关于无意识视觉和人工智能的关系：综述

Relating Blindsight and AI: A Review

Despite the impressive progress brought by deep network in visual object
recognition, robot vision is still far from being a solved problem. The most
successful convolutional architectures are developed starting from ImageNet, a
large scale collection of images of object categories downloaded from the Web.
This kind of images is very different from the situated and embodied visual
experience of robots deployed in unconstrained settings. To reduce the gap
between these two visual experiences, this paper proposes a simple yet
effective data augmentation layer that zooms on the object of interest and
simulates the object detection outcome of a robot vision system. The layer,
that can be used with any convolutional deep architecture, brings to an
increase in object recognition performance of up to 7\%, in experiments
performed over three different benchmark databases. Upon acceptance of the
paper, our robot data augmentation layer will be made publicly available.

本文提出了一种简单但有效的数据增强层，以缩小机器人视觉和常规图像分类之间的差距，并在三个不同的基准数据库实验中获得高达 7％的目标识别性能提升。