动作中的流快照神经元：深度神经网络可泛化至生物运动感知
Flow Snapshot Neurons in Action: Deep Neural Networks Generalize to Biological Motion Perception
AI总结：
针对现有AI模型生物运动感知泛化性差的问题，提出基于补丁级光流与竞争绑定机制的Motion Perceiver模型，在多类点光测试集上准确率超现有模型最高29%，且行为模式与人类契合。
摘要：
生物运动感知（BMP）指人类仅通过生物的运动模式感知并识别其动作的能力，有时这类模式极简，如点光显示器所呈现的那样。人类无需任何预先训练就能出色完成这类任务，但当前的AI模型泛化性能很差。为填补这一研究空白，我们提出了Motion Perceiver（MP，运动感知器）。MP仅以视频片段的补丁级光流作为输入。训练期间，它通过竞争绑定机制学习原型流快照，并整合不变运动表征来预测给定视频的动作标签。推理阶段，我们使用神经科学领域的点光显示器，在涵盖24种BMP条件的62656个视频刺激上评估了所有AI模型和人类的泛化能力。值得注意的是，在这些条件下的top-1动作识别准确率上，MP优于所有现有AI模型，最大提升幅度达29%。此外，我们在计算机视觉领域两个标准视频数据集的点光显示版本上对所有AI模型进行了基准测试，MP在这些场景中也表现出优越性能。更有趣的是，通过心理物理学实验我们发现，MP识别生物运动的方式与人类行为一致。我们的数据和代码可在https://github.com/ZhangLab-DeepNeuroCogLab/MotionPerceiver获取。
英文摘要：
Biological motion perception (BMP) refers to humans' ability to perceive and recognize the actions of living beings solely from their motion patterns, sometimes as minimal as those depicted on point-light displays. While humans excel at these tasks without any prior training, current AI models struggle with poor generalization performance. To close this research gap, we propose the Motion Perceiver (MP). MP solely relies on patch-level optical flows from video clips as inputs. During training, it learns prototypical flow snapshots through a competitive binding mechanism and integrates invariant motion representations to predict action labels for the given video. During inference, we evaluate the generalization ability of all AI models and humans on 62,656 video stimuli spanning 24 BMP conditions using point-light displays in neuroscience. Remarkably, MP outperforms all existing AI models with a maximum improvement of 29% in top-1 action recognition accuracy on these conditions. Moreover, we benchmark all AI models in point-light displays of two standard video datasets in computer vision. MP also demonstrates superior performance in these cases. More interestingly, via psychophysics experiments, we found that MP recognizes biological movements in a way that aligns with human behaviors. Our data and code are available at https://github.com/ZhangLab-DeepNeuroCogLab/MotionPerceiver.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
