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

Weihao Li; Yunqi Zhu; Zhihe Fan; Ruipu Zhao; Boan Tao; Xinjie Yao; Yan Fan; Pengfei Zhu

cs.CVAug 26, 2026

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

Authors: Weihao Li, Yunqi Zhu, Zhihe Fan, Ruipu Zhao, Boan Tao, Xinjie Yao, Yan Fan, Pengfei Zhu

Organizations: School of New Media and Communication, Tianjin University, Tianjin, China · School of Computer Science and Engineering, University of New South Wales, Sydney, Australia · School of Sports Training, Tianjin University of Sport, Tianjin, China · School of Artificial Intelligence, Tianjin University, Tianjin, China · School of Automation, Southeast University, Nanjing, China · School of Computer Science and Technology, Tianjin University, Tianjin, China · Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China · School of Electronic Science, National University of Defense Technology, Hunan, China

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.

Figures & tables

Figure 1: Detector-society evolution under SDL. TGRD progressively consolidates complementary expertise and reciprocally transfers it back, broadening category coverage while preserving specialization.

Figure 2: Compatibility-guided carrier trajectory planning. Held-out alignment residuals define a fixed table of directed operational scores $\widehat{D}(A,B)$ over ordered anchor–candidate pairs, from which greedy selection determines the expert order and carrier trajectory.

Figure 3: Progressive and reciprocal knowledge transfer in TGRD. Left: Along the planned trajectory, experts transfer knowledge via AFD to a carrier whose prediction head expands over accumulated categories, yielding $S_{K}$ . Right: A reciprocal detector initialized from each expert is guided by the corresponding frozen original expert and the frozen $S_{K}$ ; the updated detectors form $\mathcal{T}^{(r+1)}$ .

(a) Operational IDTD scores $\widehat{D}(a_{k-1},t_{i})$
	Candidate next expert $t_{i}$
Planning anchor $a_{k-1}$	$t_{1}$	$t_{2}$	$t_{3}$	$t_{4}$
$S_{0}$ (RetinaNet)	0.5463	1.2115	0.5588	0.6411
$S_{0}$ (Faster R-CNN)	0.5148	1.1658	0.2318	0.5473
$t_{1}$ (RetinaNet)	–	1.1792	0.5514	0.4862
$t_{2}$ (FCOS)	1.1392	–	1.0478	1.1748

Table 1: Operational IDTD scores and fixed-table greedy trajectories under two carrier initializations. Detector architectures are given in parentheses; all configurations use R50–FPN.

(a) RetinaNet as initial carrier $S_{0}$
Carrier state / control	$AP$	$AP_{\mathcal{C}_{0}}$	$AP_{\Delta\kappa_{1}}$	$AP_{\Delta\kappa_{2}}$	$AP_{\Delta\kappa_{3}}$	$AP_{\Delta\kappa_{4}}$
$S_{0}$	37.3	37.3	–	–	–	–
$S_{1}\,[t_{1}]$	35.9	39.2	22.8	–	–	–
$S_{2}\,[t_{1},t_{4}]$	35.1	40.5	22.6	26.4	–	–
$S_{3}\,[t_{1},t_{4},t_{3}]$	34.5	39.6	19.8	39.4	24.1	–
$S_{4}\,[t_{1},t_{4},t_{3},t_{2}]$	32.8	39.5	21.0	22.2	38.7	22.0

Table 2: Stage-wise carrier performance from

S_{0}

along two fixed-table trajectories versus simultaneous aggregation (Avg-FPN KD). Overall AP is evaluated on

\mathcal{K}_{k}

for

S_{k}

and on

\mathcal{C}_{\cup}

for the control, with

\mathcal{K}_{0}=\mathcal{C}_{0}

AP_{\Delta\kappa_{j}}

denotes AP over the categories introduced by

t_{\pi(j)}

that are absent from

\mathcal{K}_{j-1}

; control columns follow the same trajectory-specific order. Brackets list the incorporated experts.

	Initial support $\mathcal{C}_{i}$			Expert-specific support $\mathcal{C}_{i}\setminus\mathcal{C}_{0}$			Knowledge expansion
Expert	Original	Reciprocal	$\Delta$	Original	Reciprocal	$\Delta$	$AP_{\mathcal{C}_{\cup}\setminus\mathcal{C}_{i}}$	$AP_{\mathcal{C}_{\cup}}$
(a) Final RetinaNet carrier $S_{4}$
$t_{1}$	35.5	35.9	$+0.4$	22.5	22.3	$-0.2$	25.8	32.1
$t_{2}$	40.0	39.2	$-0.8$	31.7	30.6	$-1.1$	25.6	34.1
$t_{3}$	39.8	40.1	$+0.3$	40.2	40.7	$+0.5$	23.2	33.7
$t_{4}$	38.8	38.9	$+0.1$	26.5	27.8	$+1.3$	28.4	35.0

Table 3: Expert retention and knowledge expansion after reciprocal updates jointly guided by the original experts and final (a) RetinaNet or (b) Faster R-CNN carriers.

\Delta

denotes the reciprocal-minus-original AP change.

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Symbiosis-Inspired Knowledge Distillation for Incremental Object Detection

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Class-Incremental Learning Object Detection Knowledge Distillation Catastrophic Forgetting

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Incremental object detection (IOD) aims to extend detectors to new categories while retaining previously acquired knowledge. Existing methods often adopt a class incremental learning perspective, separating feature spaces to sharpen decision boundaries. However, this separation-oriented paradigm may overlook object symbiosis in detection, where co-occurrence and occlusion introduce spatial and semantic dependencies that benefit from shared representations. Ignoring these dependencies distorts the shared representations, exacerbates confusion between old and new classes, and accelerates catastrophic forgetting. To address this, we propose Symbiosis-Inspired Knowledge Distillation (SIKD), which explicitly leverages object symbiosis at two complementary levels. Spatial Symbiosis Distillation (SpSD) focuses on symbiotic regions where the old model responds with high overlap to objects in the new task. It preserves generalizable old class cues, suppresses class-specific bias and redundancy, and distills the refined evidence to the new model at matched spatial locations with slot-aligned supervision. Semantic Symbiosis Distillation (SeSD) maintains class level structure by forming confidence weighted prototypes for old classes and aligning their inter class soft ranks over the old class logits, which stabilizes the semantic topology during adaptation. Extensive experiments demonstrate the effectiveness and superiority of the proposed method.

Abstract

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