# StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection Source: [StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:46:17.527Z Authors: Sanghyun Woo, Soonmin Hwang, In So Kweon Published: 2017-09-18T06:38:10.000Z ## Abstract One-stage object detectors such as SSD or YOLO already have shown promising accuracy with small memory footprint and fast speed. However, it is widely recognized that one-stage detectors have difficulty in detecting small objects while they are competitive with two-stage methods on large objects. In this paper, we investigate how to alleviate this problem starting from the SSD framework. Due to their pyramidal design, the lower layer that is responsible for small objects lacks strong semantics(e.g contextual information). We address this problem by introducing a feature combining module that spreads out the strong semantics in a top-down manner. Our final model StairNet detector unifies the multi-scale representations and semantic distribution effectively. Experiments on PASCAL VOC 2007 and PASCAL VOC 2012 datasets demonstrate that StairNet significantly improves the weakness of SSD and outperforms the other state-of-the-art one-stage detectors. ## 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. - [Object Detection on Open Images 2.4K fashion photos V4 (test)](): Object Detection; Open Images 2.4K fashion photos V4 (test); mAP: 64.8. - [Object Detection on PASCAL VOC 2007 (test)](): Object Detection; PASCAL VOC 2007 (test); mAP: 78.9. ## Original sources and implementation [Original paper]() [PDF]()