Attention and Selection Mechanisms in Neural Networks and Computer Vision

神经网络和计算机视觉中的注意和选择机制

• 批准号: RGPIN-2019-05777
• 负责人: Bruce, Neil
• 金额: $ 2.04万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=715809
• 关键词: Attention; Selection; Mechanisms; Neural; Networks

The proposed research is comprised of mechanisms for attention, selection, gating and routing of information in neural information processing systems including sequential solutions to visual problems. Recurrent deep learning models form the primary construct for demonstrating the value and utility of the proposed corpus of work. While deep learning models have shown considerable success on a wide range of problems, there remains a need for better characterization of their behaviour, and innovation in directions that produce solutions that are both more parsimonious and effective in addressing their objectives. To this end, this proposal encapsulates a comprehensive body of theoretical and experimental work to meet these objectives. This is achieved through two distinct but complementary pillars as a foundation: First, we propose to examine in detail recurrent gating mechanisms that control flow of information through neural hierarchies. This includes additive. multiplicative and other gating mechanisms, their relation to gain control and normalization strategies, and connections to canonical mechanisms that appear in common feed-forward networks. My research group has shown significant potential for such control mechanisms for the capabilities of deep neural networks. Second, we address the need to consider solutions to problems that include sequential or temporal identification of relevant patterns or salient regions. Vision is inherently sequential with the most palpable example of this in humans involving head and eye movements. Equally important is the dynamic nature of processing within the brain including the role of attention. To date, many problems in computer vision operate in a manner that produces pixel-wise assignments to an image in one pass. This second pillar involves careful examination of attentive selection in space, and recurrent mechanisms for information processing to facilitate processing for vision. Products of this investigations will include sequential models of gaze patterns, solution to vision problems in a sequential manner, relationships between focal processing and recurrent gating and comparison of human an machine vision solutions to spatial sampling. Each facet of this work begins with establishing basic principles of importance as a foundation while examining practical applicability in recurrent networks and also drawing connections to attention mechanisms. As a whole, the proposed research stands to provide fundamental contributions to deep learning from an architectural perspective and at the level of augmenting our understanding of information processing capabilities of neural networks. Moreover, the research extends the status quo into a broader set of sequential or temporal solutions to machine vision problems, as an alternative paradigm for problem solving and in a form amenable to active vision systems.

研究内容包括神经信息处理系统中信息的注意、选择、门控和路由机制，包括视觉问题的顺序解决方案。循环深度学习模型构成了展示所提议的工作语料库的价值和效用的主要结构。