PSEE：用于点监督时间动作定位的渐进式传感器事件扩展
PSEE: Progressive Sensor Event Expansion for Point-Supervised Temporal Action Localization
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中文总结 AI 辅助
针对点监督时间动作定位中伪片段质量差的问题，提出PSEE方法，结合语义激活、传感器转换证据和自适应时间归属生成伪片段，在四个惯性基准上提升边界质量并兼容多种检测器。
中文摘要 AI 辅助
可穿戴传感器流中的时间动作定位（TAL）识别动作类别和时间边界，比传统动作识别实现更细粒度的活动理解。然而，训练通常需要对每个动作实例进行昂贵的开始-结束标注。为减轻此负担，我们研究点监督TAL，其中每个实例仅用一个时间戳及其类别进行标注。我们提出渐进式传感器事件扩展（PSEE），它结合语义激活、传感器特定转换证据和自适应时间归属来恢复点监督伪片段。这些片段监督标准TAL检测器，无需修改其推理过程。在四个惯性传感基准上的跨主体实验表明，与改编的点监督基线相比，伪边界质量得到改善，与不同TAL检测器兼容，并对点采样具有鲁棒性。代码可在https URL获取。
英文摘要
Temporal action localization (TAL) in wearable sensor streams identifies action classes and temporal boundaries, enabling finer-grained activity understanding than conventional action recognition. However, training typically requires costly start--end annotations for every action instance. To reduce this burden, we study point-supervised TAL, where each instance is labeled with only one timestamp and its class. We propose Progressive Sensor Event Expansion (PSEE), which combines semantic activations, sensor-specific transition evidence, and adaptive temporal ownership to recover point-supervised pseudo segments. These segments supervise standard TAL detectors without modifying their inference procedures. Cross-subject experiments on four inertial-sensing benchmarks demonstrate improved pseudo-boundary quality over adapted point-supervised baselines, compatibility with different TAL detectors, and robustness to point sampling. Code is available at https://github.com/joeeeeyin/PSEE.
