Ventral-Dorsal Neural Networks: Object Detection via Selective Attention

Early recurrence enables figure border ownership

The face-vase illusion introduced by Rubin demonstrates how one can switch back and forth between two different interpretations depending on how the figure outlines are assigned [1]. This border ownership assignment is an important step in the perception of forms. Zhou et al. [2] found neurons in the visual cortex whose responses not only depend on the local features present in their classical receptive fields, but also on their contextual information. Various models proposed that feedback from higher ventral areas or lateral connections could provide the required contextual information. Howev…

BIMM：用于视频表示学习的脑启发掩码建模

BIMM: Brain Inspired Masked Modeling for Video Representation Learning

该研究受人脑腹侧与背侧视觉通路启发，提出BIMM框架，通过双分支掩码建模、渐进式重建和部分参数共享学习视频表示，并取得优于现有方法的性能。

迈向基于双流注视的主动视觉学习

Towards Two-Stream Foveation-based Active Vision Learning

受神经科学双流假说启发，提出一种结合腹侧和背侧双流及注视机制的主动视觉学习框架，通过标签训练和强化学习实现弱监督目标定位及跨数据集泛化。

Deep Neural Networks predict Hierarchical Spatio-temporal Cortical Dynamics of Human Visual Object Recognition

The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient visual object recognition in humans. However, the stage-wise computations therein remain poorly understood. Here, we compared temporal (magnetoencephalography) and spatial (functional MRI) visual brain representations with representations in an artificial deep neural network (DNN) tuned to the statistics of real-world visual recognition. We showed that the DNN captured the stages of human visual processing in both time and space from early visual areas towards the dorsal and ventral streams…

Learning to attend in a brain-inspired deep neural network

Recent machine learning models have shown that including attention as a component results in improved model accuracy and interpretability, despite the concept of attention in these approaches only loosely approximating the brain's attention mechanism. Here we extend this work by building a more brain-inspired deep network model of the primate ATTention Network (ATTNet) that learns to shift its attention so as to maximize the reward. Using deep reinforcement learning, ATTNet learned to shift its attention to the visual features of a target category in the context of a search task. ATTNet's dors…

Using an AI creativity system to explore how aesthetic experiences are processed along the brains perceptual neural pathways

With the increased sophistication of AI techniques, the application of these systems has been expanding to ever newer fields. Increasingly, these systems are being used in modeling of human aesthetics and creativity, e.g. how humans create artworks and design products. Our lab has developed one such AI creativity deep learning system that can be used to create artworks in the form of images and videos. In this paper, we describe this system and its use in studying the human visual system and the formation of aesthetic experiences. Specifically, we show how time-based AI created media can be us…

Bio-inspired Unsupervised Learning of Visual Features Leads to Robust Invariant Object Recognition

Retinal image of surrounding objects varies tremendously due to the changes in position, size, pose, illumination condition, background context, occlusion, noise, and nonrigid deformations. But despite these huge variations, our visual system is able to invariantly recognize any object in just a fraction of a second. To date, various computational models have been proposed to mimic the hierarchical processing of the ventral visual pathway, with limited success. Here, we show that the association of both biologically inspired network architecture and learning rule significantly improves the mod…

使用深度卷积神经网络建模生物人脸识别

Modeling biological face recognition with deep convolutional neural networks

本综述总结了首批用深度卷积神经网络建模生物人脸识别的研究，论证其与脑内面孔加工层级结构的相似性，揭示其在面孔检测与识别机制研究中的独特价值，可为相关长期争议提供参考。