The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
FCOS: Fully Convolutional One-Stage Object Detection
Open accessAbstract
We propose a fully convolutional one-stage object detector (FCOS) to solve object detection in a per-pixel prediction fashion, analogue to semantic segmentation. Almost all state-of-the-art object detectors such as RetinaNet, SSD, YOLOv3, and Faster R-CNN rely on pre-defined anchor boxes. In contrast, our proposed detector FCOS is anchor box free, as well as proposal free. By eliminating the pre-defined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating overlapping during training. More importantly, we also avoid all hyper-parameters related to anchor boxes, which are often very sensitive to the final detection performance. With the only post-processing non-maximum suppression (NMS), FCOS with ResNeXt-64x4d-101 achieves 44.7% in AP with single-model and single-scale testing, surpassing previous one-stage detectors with the advantage of being much simpler. For the first time, we demonstrate a much simpler and flexible detection framework achieving improved detection accuracy. We hope that the proposed FCOS framework can serve as a simple and strong alternative for many other instance-level tasks. Code is available at: https://tinyurl.com/FCOSv1.
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“Inspired by [41], we introduced a feature pyramid decoder (FPD) to better aggregate multi-resolution features at different network levels for grading the placental maturity.”
From DilatedFormer: dilated granularity transformer network for placental maturity grading in ultrasound (2023) · cites this paper as [41]
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“That is, they predict 3D information like object pose and velocity using an object detection pipeline designed for 2D tasks (e.g., CenterNet [1], FCOS [3]), without considering 3D scene structure or sensor configuration.”
From DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries (2021) · cites this paper as [3]
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PolarMask: Single Shot Instance Segmentation with Polar RepresentationPreprint
Xie, E., Sun, P., Song, X., Wang, W., Liang, D., Shen, C., & Luo, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.13226 · Cited by 62
Self-citationLi, Z., Wang, W., Li, H., Xie, E., Sima, C., Lu, T., Yu, Q., & Dai, J. (2022) · arXiv · doi:10.48550/arxiv.2203.17270 · Cited by 1,419
“Following previous methods [45, 47, 31], we adopt two types of backbone: ResNet101-DCN [15, 12] that initialized from FCOS3D [45] checkpoint, and VoVnet-99 [21] that initialized from DD3D [31] checkpoint.”
Efficient and Lightweight Framework for Real-Time Ore Image Segmentation Based on Deep Learning
Sun, G., Huang, D., Cheng, L., Jia, J., Xiong, C., & Zhang, Y. (2022) · Minerals · doi:10.3390/min12050526 · Cited by 24
Rosbach, E., Ammeling, J., Krügel, S., Kießig, A., Fritz, A., Ganz, J., Puget, C., Donovan, T., Klang, A., Köller, M. C., et al. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713319 · Cited by 18
QBox: Partial Transfer Learning With Active Querying for Object Detection
Tang, Y.-P., Wei, X.-S., Zhao, B., & Huang, S.-J. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3111621 · Cited by 17
Non-Maximum Suppression Guided Label Assignment for Object Detection in Crowd Scenes
Jiang, H., Zhang, X., & Xiang, S. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3293333 · Cited by 16
Sim-to-Real Transfer for Object Detection in Aerial Inspections of Transmission Towers
Peterlevitz, A. J., Chinelatto, M. A., Menezes, A. G., Motta, C. A. M., Pereira, G. A. B., Lopes, G. L., Souza, G. D. M., Rodrigues, J., Godoy, L. C., Koller, M. A. F. F., et al. (2023) · IEEE Access · doi:10.1109/access.2023.3322374 · Cited by 7
“However, new one-stage detectors, such as FCOS [25] and YOLOv5 [26], have shown that one-stage solutions are also suitable for performance-dependent tasks.”
Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models
Jocher, G., Qiu, J., Liu, M., Lyu, S., Akyon, F. C., & Kalfaoglu, M. E. (2026) · arXiv · doi:10.48550/arxiv.2606.03748 · Cited by 16
“Anchor-free designs such as FCOS [66] and Ultra-lytics YOLOv8 [69] further simplified the detection head, while YOLOv10 [78] introduced consistent dual assignments to enable NMS-free inference.”
DilatedFormer: dilated granularity transformer network for placental maturity grading in ultrasound
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YOLOX: Exceeding YOLO Series in 2021
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MMDetection: Open MMLab Detection Toolbox and Benchmark
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Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection
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Learning Spatial Fusion for Single-Shot Object Detection
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Efficient DETR: Improving End-to-End Object Detector with Dense Prior
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SOLQ: Segmenting Objects by Learning Queries
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Metadata
- Authors
- Tian, Z., Shen, C., Chen, H., & He, T.
- Affiliations
- The University of Adelaide (AU)
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 9626-9635
- OpenAlex
- W2982770724
- Cited by
- 7,013
- References
- 34
- Citations by year
- 2026: 591 · 2025: 977 · 2024: 1403 · 2023: 1476 · 2022: 1212 · 2021: 921 · 2020: 386 · 2019: 45
- FWCI
- 209.06
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- FCOS, one-stage object detection, anchor-free detection, fully convolutional network, non-maximum suppression, instance segmentation
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