# Selective Multi-Scale Learning for Object Detection

Source: [Selective Multi-Scale Learning for Object Detection](<https://www.sota2.com/research/paper/68fa6cc01e209131cea35cb3>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:17:38.470Z

Authors: Junliang Chen, Weizeng Lu, Linlin Shen

Published: 2022-06-16T14:23:50.000Z

## Abstract

Pyramidal networks are standard methods for multi-scale object detection. Current researches on feature pyramid networks usually adopt layer connections to collect features from certain levels of the feature hierarchy, and do not consider the significant differences among them. We propose a better architecture of feature pyramid networks, named selective multi-scale learning (SMSL), to address this issue. SMSL is efficient and general, which can be integrated in both single-stage and two-stage detectors to boost detection performance, with nearly no extra inference cost. RetinaNet combined with SMSL obtains 1.8\\% improvement in AP (from 39.1\\% to 40.9\\%) on COCO dataset. When integrated with SMSL, two-stage detectors can get around 1.0\\% improvement in AP.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2206.08206>)

[PDF](<https://arxiv.org/pdf/2206.08206.pdf>)