# CoupleNet: Coupling Global Structure with Local Parts for Object Detection

arXiv: https://arxiv.org/abs/1708.02863 · HTML: https://mlsanity.com/paper/1708.02863/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2017.

## Built on by (most cited first, 16 of 16)

- [Hallucination In Object Detection -- A Study In Visual Part Verification](https://mlsanity.com/paper/2106.02523/) (2021)
- [Precise Single-stage Detector](https://mlsanity.com/paper/2210.04252/) (2022)
- [Scale Normalized Image Pyramids with AutoFocus for Object Detection](https://mlsanity.com/paper/2102.05646/) (2021)
- [VISLIX: An XAI Framework for Validating Vision Models with Slice Discovery and Analysis](https://mlsanity.com/paper/2505.03132/) (2025)
- [Evaluating Context for Deep Object Detectors](https://mlsanity.com/paper/2205.02887/) (2022)
- [A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets](https://mlsanity.com/paper/2107.07927/) (2021)
- [HoughNet: Integrating near and long-range evidence for visual detection](https://mlsanity.com/paper/2104.06773/) (2021)
- [OCHID-Fi: Occlusion-Robust Hand Pose Estimation in 3D via RF-Vision](https://mlsanity.com/paper/2308.10146/) (2023)
- [Exploring Contextual Relationships for Cervical Abnormal Cell Detection](https://mlsanity.com/paper/2207.04693/) (2022)
- [Seismic-phase detection using multiple deep learning models for global and local representations of waveforms](https://mlsanity.com/paper/2211.02261/) (2022)
- [Cluster-CAM: Cluster-Weighted Visual Interpretation of CNNs' Decision in Image Classification](https://mlsanity.com/paper/2302.01642/) (2023)
- [Advancement of Deep Learning in Pneumonia and Covid-19 Classification and Localization: A Qualitative and Quantitative Analysis](https://mlsanity.com/paper/2111.08606/) (2021)
- [RADNet: A Deep Neural Network Model for Robust Perception in Moving Autonomous Systems](https://mlsanity.com/paper/2205.00364/) (2022)
- [Stall Number Detection of Cow Teats Key Frames](https://mlsanity.com/paper/2303.10444/) (2023)
- [YOLO-Former: YOLO Shakes Hand With ViT](https://mlsanity.com/paper/2401.06244/) (2024)
- [Generalized Event Partonomy Inference with Structured Hierarchical Predictive Learning](https://mlsanity.com/paper/2512.04219/) (2025)

## Abstract (arXiv)

The region-based Convolutional Neural Network (CNN) detectors such as Faster R-CNN or R-FCN have already shown promising results for object detection by combining the region proposal subnetwork and the classification subnetwork together. Although R-FCN has achieved higher detection speed while keeping the detection performance, the global structure information is ignored by the position-sensitive score maps. To fully explore the local and global properties, in this paper, we propose a novel fully convolutional network, named as CoupleNet, to couple the global structure with local parts for object detection. Specifically, the object proposals obtained by the Region Proposal Network (RPN) are fed into the the coupling module which consists of two branches. One branch adopts the position-sensitive RoI (PSRoI) pooling to capture the local part information of the object, while the other employs the RoI pooling to encode the global and context information. Next, we design different coupling strategies and normalization ways to make full use of the complementary advantages between the global and local branches. Extensive experiments demonstrate the effectiveness of our approach. We achieve state-of-the-art results on all three challenging datasets, i.e. a mAP of 82.7% on VOC07, 80.4% on VOC12, and 34.4% on COCO. Codes will be made publicly available.