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arXiv Computer Vision By Weihao Li, Yunqi Zhu, Zhihe Fan, Ruipu Zhao, Boan Tao, Xinjie Yao, Yan Fan, Pengfei Zhu

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

Read the original on arXiv Computer Vision →

The paper introduces Socialized Detector Learning (SDL), a framework for improving heterogeneous, category-specialized object detectors by exchanging knowledge within a detector society. It proposes Trajectory-Guided and Reciprocal Distillation (TGRD), which estimates transfer difficulty, constructs a carrier trajectory, and performs progressive consolidation and reciprocal transfer. Experiments on MS COCO show that carriers outperform simultaneous aggregation by 2.6 AP, and reciprocal detectors achieve high AP on previously unsupported categories while staying close to expert performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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