让不同检测器协作进化，通过定向知识传递提升整体性能。
Socialized Detector Learning: Trajectory-Guided and Reciprocal Distillation for Heterogeneous Object Detectors
- 基于检测器间转移难度构建知识传递路径，分步融合能力。
- 在COCO上比并行聚合提升2.6 AP，新类别检测达20.8–28.4 AP。
- 适合多专家协同、需跨类别泛化的检测系统部署场景。
目标检测知识分散于独立训练的异构检测器中，各自覆盖不同类别。在社会学习框架下，这些检测器构成一个集体，学习目标是通过知识交换共同演化。然而，现有聚合式社会学习未规划传递顺序，而渐进式多教师蒸馏虽考虑顺序，却仅单向增强学生模型。为此，本文提出异构检测器社会学习（SDL）框架与轨迹引导的双向蒸馏（TGRD）。TGRD通过预留特征对齐残差估算检测器间转移难度，预计算固定评分表，并贪婪构建知识传递路径。沿路径，知识逐步整合至联合类别载体，再通过双向回传返回各专家。条件代理证书分析表明，在假设条件下，渐进式证书规模不超过聚合目标。在四个异构专家和两种初始化的MS COCO实验中，最终载体在两种设置下均比同期聚合基准高出2.6 AP。双向检测器在原有不支持类别上取得20.8–28.4 AP，且原专家性能仅下降1.3 AP以内。结果验证了有序渐进融合结合双向回传是检测器社会演化的有效机制。
原文摘要 · Abstract (English)
Object detection knowledge is fragmented across independently trained, heterogeneous detectors with complementary category supports. In socialized learning, this knowledge resides in a society, and learning aims to evolve the society collectively through exchange. However, aggregation-based socialization does not explicitly plan transfer order, whereas progressive multi-teacher distillation considers order but remains a one-way student enhancement in a shared category space. Building on Socialized Learning, we formulate Socialized Detector Learning (SDL) for heterogeneous, category-specialized object detectors and propose Trajectory-Guided and Reciprocal Distillation (TGRD).TGRD estimates directed operational Inter-Detector Transfer Difficulty (IDTD) from held-out feature-alignment residuals, precomputes a fixed score table, and greedily constructs a carrier trajectory. Along the trajectory, knowledge is progressively consolidated into a union-category carrier and then returned to experts through reciprocal transfer. A conditional proxy-certificate analysis shows that, under stated assumptions, the progressive certificate is no larger than an aggregated-target counterpart. On MS COCO with four heterogeneous experts and two carrier initializations, final carriers outperform epoch-matched simultaneous aggregation controls by 2.6 AP in both settings. Reciprocal detectors attain 20.8--28.4 AP on previously unsupported categories while remaining within 1.3 AP of original expert-specific performance. These results support order-aware progressive consolidation followed by reciprocal transfer as a viable mechanism for detector-society evolution.
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