用相位同步机制让模型像人一样跟踪变脸物体
Tracking objects that change in appearance with phase synchrony
- 用复数递归网络通过相位同步分离注意力与外观
- 在可控变化的追踪任务中，模型表现接近人类水平
- 适合研究视觉注意与动态物体追踪的学者
我们日常观察的物体常因光照、姿态或非刚性运动而改变外观。生物视觉系统如何在外观变化下持续追踪物体？一种可能机制是通过注意机制独立于外观定位物体，这与神经同步计算相关。本文提出一种新型深度学习电路——复数递归神经网络（CV-RNN），可通过神经相位同步实现对特征位置的独立注意力控制。我们通过FeatureTracker这一大规模挑战任务，比较人类、CV-RNN与其他深度神经网络（DNNs）的追踪表现。结果发现，人类能轻松完成任务，而现有先进DNNs表现不佳；相比之下，CV-RNN的表现与人类相似，为相位同步作为外观变化物体追踪的神经基础提供了计算验证。
原文摘要 · Abstract (English)
Objects we encounter often change appearance as we interact with them. Changes in illumination (shadows), object pose, or the movement of non-rigid objects can drastically alter available image features. How do biological visual systems track objects as they change? One plausible mechanism involves attentional mechanisms for reasoning about the locations of objects independently of their appearances -- a capability that prominent neuroscience theories have associated with computing through neural synchrony. Here, we describe a novel deep learning circuit that can learn to precisely control attention to features separately from their location in the world through neural synchrony: the complex-valued recurrent neural network (CV-RNN). Next, we compare object tracking in humans, the CV-RNN, and other deep neural networks (DNNs), using FeatureTracker: a large-scale challenge that asks observers to track objects as their locations and appearances change in precisely controlled ways. While humans effortlessly solved FeatureTracker, state-of-the-art DNNs did not. In contrast, our CV-RNN behaved similarly to humans on the challenge, providing a computational proof-of-concept for the role of phase synchronization as a neural substrate for tracking appearance-morphing objects as they move about.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。