RFAssigner: A Generic Label Assignment Strategy for Dense Object Detection

• URL: http://arxiv.org/abs/2601.01240v1
• Date: Sat, 03 Jan 2026 17:19:41 GMT
• Title: RFAssigner: A Generic Label Assignment Strategy for Dense Object Detection
• Authors: Ziqian Guan, Xieyi Fu, Yuting Wang, Haowen Xiao, Jiarui Zhu, Yingying Zhu, Yongtao Liu, Lin Gu,
• Abstract summary: State-of-the-art methods typically assign each training sample a positive and a negative weight, optimizing the assignment scheme during training.<n>We introduce RFer, a novel assignment strategy designed to enhance the multi-scale learning capabilities of dense detectors.<n>RFer adaptively selects supplementary positive samples from the unassigned pool, promoting a more balanced learning process across object scales.
• Score: 9.226320199517259
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Label assignment is a critical component in training dense object detectors. State-of-the-art methods typically assign each training sample a positive and a negative weight, optimizing the assignment scheme during training. However, these strategies often assign an insufficient number of positive samples to small objects, leading to a scale imbalance during training. To address this limitation, we introduce RFAssigner, a novel assignment strategy designed to enhance the multi-scale learning capabilities of dense detectors. RFAssigner first establishes an initial set of positive samples using a point-based prior. It then leverages a Gaussian Receptive Field (GRF) distance to measure the similarity between the GRFs of unassigned candidate locations and the ground-truth objects. Based on this metric, RFAssigner adaptively selects supplementary positive samples from the unassigned pool, promoting a more balanced learning process across object scales. Comprehensive experiments on three datasets with distinct object scale distributions validate the effectiveness and generalizability of our method. Notably, a single FCOS-ResNet-50 detector equipped with RFAssigner achieves state-of-the-art performance across all object scales, consistently outperforming existing strategies without requiring auxiliary modules or heuristics.

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