CAREER: Computational mechanisms of rapid visual categorization: Models and psychophysics
职业:快速视觉分类的计算机制:模型和心理物理学
基本信息
- 批准号:1252951
- 负责人:
- 金额:$ 50万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2013
- 资助国家:美国
- 起止时间:2013-09-15 至 2019-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Primates can recognize objects embedded in complex natural visual scenes at a glance. Despite the ease with which we see, visual recognition -- one of the key issues addressed in computer vision -- is quite difficult for machines. Understanding which computations are performed by the visual cortex would give scientists a powerful tool to uncover key mechanisms of human perception and cognition as well as to create a new generation of 'seeing' machines. The PI's central research goal is to identify the perceptual principles and model the neural mechanisms underlying rapid visual categorization. By forcing processing to be fast, rapid visual categorization paradigms help isolate the very first pass of visual information before more complex visual routines take place. Hence, understanding 'vision at a glance' is arguably a necessary first step before studying natural everyday vision where eye movements and attentional shifts are known to play a key role.Specifically, this proposal will lead to the development of a computational neuroscience model of rapid visual recognition in the primate visual system, which is both consistent with physiological properties of cells in the visual cortex and able to predict behavioral responses (both correct and incorrect responses as well as reaction times) from human participants across a range of conditions. The proposed model will integrate recent developments in computational models of vision and decision making with large-scale machine learning techniques. New stimulus sets will be generated, which are optimally tailored for testing among alternative visual representations and computations against human psychophysics data. These experiments will, in turn, enable the refinement of computational models.The computational models developed as part of this proposal will be integrated in courses and disseminated broadly via a web graphical interface. Overall the interdisciplinary nature of the proposal will give students the opportunity to experience a research environment that crosses traditional boundaries across disciplines and departments. Increased undergraduate participation in computational neuroscience will help integrate this area into the mainstream computer science and neuroscience curricula.
灵长类动物一眼就能识别出嵌入在复杂自然视觉场景中的物体。尽管我们看起来很容易,但视觉识别-计算机视觉中解决的关键问题之一-对机器来说相当困难。了解哪些计算是由视觉皮层执行的,将为科学家提供一个强大的工具,以揭示人类感知和认知的关键机制,并创造新一代的“看”机器。PI的中心研究目标是识别感知原则,并建立快速视觉分类的神经机制模型。快速视觉分类范式通过迫使处理快速,有助于在更复杂的视觉例程发生之前隔离视觉信息的第一次传递。因此,在研究自然的日常视觉之前,理解“一眼视觉”可以说是必要的第一步,其中眼睛运动和注意力转移已知发挥关键作用。具体来说,这一提议将导致灵长类视觉系统中快速视觉识别的计算神经科学模型的发展,这既符合视觉皮层细胞的生理特性,(正确和不正确的反应以及反应时间)。该模型将整合视觉和决策计算模型的最新发展与大规模机器学习技术。新的刺激集将被生成,这是最佳定制的测试之间的替代视觉表示和计算对人类心理物理学数据。这些实验反过来将使计算模型得以改进,作为本提案一部分开发的计算模型将纳入课程,并通过网络图形界面广泛传播。总体而言,该提案的跨学科性质将使学生有机会体验跨越学科和部门传统界限的研究环境。增加本科生参与计算神经科学将有助于将这一领域纳入主流计算机科学和神经科学课程。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Thomas Serre其他文献
1 AUTOMATED HOME-CAGE BEHAVIORAL PHENOTYPING OF MICE
1 小鼠自动笼养行为表型分析
- DOI:
- 发表时间:
2009 - 期刊:
- 影响因子:0
- 作者:
Thomas Serre;Huei;Estibaliz Garrote;Xinlin Yu;Vinita Khilnani;Tomaso A. Poggio;Andrew D. Steele - 通讯作者:
Andrew D. Steele
Feature Selection for Face Detection
人脸检测的特征选择
- DOI:
- 发表时间:
2000 - 期刊:
- 影响因子:0
- 作者:
Thomas Serre;B. Heisele;Sayan Mukherjee;T. Poggio - 通讯作者:
T. Poggio
Learning complex cell invariance from natural videos: A plausibility proof
从自然视频中学习复杂的细胞不变性:合理性证明
- DOI:
10.21236/ada477541 - 发表时间:
2007 - 期刊:
- 影响因子:8
- 作者:
T. Masquelier;Thomas Serre;S. Thorpe;T. Poggio - 通讯作者:
T. Poggio
Xplique: A Deep Learning Explainability Toolbox
Xplique:深度学习可解释性工具箱
- DOI:
10.48550/arxiv.2206.04394 - 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Thomas Fel;Lucas Hervier;David Vigouroux;Antonin Poche;Justin Plakoo;Rémi Cadène;Mathieu Chalvidal;Julien Colin;Thibaut Boissin;Louis Béthune;Agustin Picard;C. Nicodeme;L. Gardes;G. Flandin;Thomas Serre - 通讯作者:
Thomas Serre
Thomas Serre的其他文献
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{{ truncateString('Thomas Serre', 18)}}的其他基金
CRCNS US-France Research Proposal: Oscillatory processes for visual reasoning in deep neural networks
CRCNS 美国-法国研究提案:深度神经网络中视觉推理的振荡过程
- 批准号:
1912280 - 财政年份:2019
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
Collaborative Research: Origins of Southeast Asian Rainforests from Paleobotany and Machine Learning
合作研究:古植物学和机器学习的东南亚雨林起源
- 批准号:
1925481 - 财政年份:2019
- 资助金额:
$ 50万 - 项目类别:
Continuing Grant
I-Corps: Development of a machine vision system for high-throughput computational behavioral analysis
I-Corps:开发用于高通量计算行为分析的机器视觉系统
- 批准号:
1644560 - 财政年份:2016
- 资助金额:
$ 50万 - 项目类别:
Standard Grant
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