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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The feature pyramid decoder better aggregates multi-resolution features at different network levels for grading placental maturity.
“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]
Existing 2D-based methods predict 3D information like object pose and velocity using an object detection pipeline designed for 2D tasks (e.g., CenterNet, FCOS), without considering 3D scene structure or sensor configuration.
“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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FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection
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PETR: Position Embedding Transformation for Multi-view 3D Object Detection
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Self-citationDETRs with Collaborative Hybrid Assignments Training
Zong, Z., Song, G., & Liu, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00621 · Cited by 650 · see citing works
Slicing Aided Hyper Inference and Fine-Tuning for Small Object Detection
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DiffusionDet: Diffusion Model for Object Detection
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FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding
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Poly Kernel Inception Network for Remote Sensing Detection
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A review of object detection based on deep learning
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End-to-End Semi-Supervised Object Detection with Soft Teacher
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A Survey of Visual Transformers
Liu, Y., Zhang, Y., Wang, Y., Hou, F., Yuan, J. S., Tian, J., Zhang, Y. S., Shi, Z., Jianping Fan, B., & He, Z. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3227717 · Cited by 614 · see citing works
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“In this paper, we will implement our QueryDet based on RetinaNet and FCOS [50], to show the effectiveness and generalization of our method.”
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Channel-wise Knowledge Distillation for Dense Prediction
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- 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 · 2018: 1
- 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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