Socialized Detector Learning: Trajectory-Guided and Reciprocal Distillation for Heterogeneous Object Detectors

📅 2026-08-26
📈 Citations: 0
Influential: 0
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🤖 AI Summary
研究通过轨迹引导和互惠蒸馏方法,解决了异构物体检测器间知识碎片化问题,实现检测器社会的集体进化。
📝 Abstract
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.
Problem

Research questions and friction points this paper is trying to address.

heterogeneous detectors
knowledge fragmentation
socialized learning
reciprocal transfer
Innovation

Methods, ideas, or system contributions that make the work stand out.

Socialized Detector Learning
Trajectory-Guided and Reciprocal Distillation
Inter-Detector Transfer Difficulty
Progressive Consolidation
Reciprocal Transfer
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Yunqi Zhu
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Zhihe Fan
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Pengfei Zhu
Professor, College of Intelligence and Computing , Tianjin University
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