RI: Medium: Inverse Reinforcement Learning for Human Attention Modeling

人们把注意力从一件事转移到另一件事的过程涉及到我们所想和所做的一切，因此在从基础研究和教育到工业和国防应用等领域具有广泛的重要性。这项研究开发了一个计算模型来预测人类视觉注意力的这些转移。预测就是理解，有了这个模型，我们将更好地理解人类的这一核心认知过程。更具体的是，预测使应用程序能够预测在看到特定图像时注意力将转移到哪里。这反过来将带来1)新一代人机交互系统，能够在用户的注意力移动水平上与用户交互，以及2)基于注意力重要性或兴趣对视觉内容进行注释和索引的新颖方法。该项目将计算工作与认知科学和数字媒体相结合，通过宝贵的学习经验为妇女和代表不足的少数群体提供了进入计算机科学的途径，否则他们可能会被传统的计算工作吓倒。该项目通过与石溪大学正在进行的几项努力，包括女性科学与工程组织(WISE)和路易斯·斯托克斯少数群体参与联盟(LSAMP)的合作，扩大了未被充分代表的少数群体接触STEM的机会。该项目研究了一种协同计算和行为方法来模拟人类注意力的运动。这种方法基于这样一种假设，即对图像(或视频帧)的注意力参与取决于正在观看的像素和观看者的先前状态。基于这一假设，将视觉注意假设为马尔可夫决策过程，并使用逆强化学习来学习奖励函数，从而将图像中特定的时空区域(对应于观看者瞬间注意位置的像素)与奖励相关联。在这种新的方法下，注意机制被视为一个智能体，其行为是在图像或图像帧中选择一个位置，以最大化其总回报。这一模型正在根据行为基础事实进行评估，该事实包括人们在自由观看和视觉搜索任务中观看图像和视频时的眼球运动。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The process by which people shift their attention from one thing to another touches upon everything that we think and do, and as such has widespread importance in fields ranging from basic research and education to applications in industry and national defense. This research develops a computational model for predicting these human shifts in visual attention. Prediction is understanding, and with this model we will achieve a greater understanding of this core human cognitive process. More tangibly, prediction enables applications to anticipate where attention will shift in response to seeing specific imagery. This in turn would usher in 1) a new generation of human-computer interactive systems, ones capable of interacting with users at the level of their attention movements, and 2) novel ways to annotate and index visual content based on attentional importance or interest. This project combines computational work with cognitive science and digital media, providing an entry to computer science through valuable learning experiences for women and underrepresented minorities who might otherwise be intimidated by traditional computational work. The project broadens the exposure of underrepresented minorities to STEM through partnerships with several ongoing efforts at Stony Brook University, including the Women in Science and Engineering (WISE) and Louis Stokes Alliance for Minority Participation (LSAMP) initiatives.This project investigates a synergistic computational and behavioral approach for modeling the movements of human attention. This approach is based on an assumption that attentional engagement on an image (or video frame) depends on both the pixels that are being viewed and the viewer's previous state. Based on this assumption, visual attention is posed as a Markov decision process, and inverse reinforcement learning is used to learn a reward function to associate specific spatio-temporal regions in an image, corresponding to the pixels at a viewer's momentary locus of attention, with a reward. Under this novel approach, the attention mechanism is treated as an agent whose action is to select a location in an image or image frame that will maximize its total reward. This model is being evaluated against a behavioral ground truth consisting of the eye movements that people make as they view images and video in the context of free viewing and visual search tasks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.