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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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BackgroundDeep High-Resolution Representation Learning for Visual Recognition
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“Method Backbone TTA AP AP50 AP75 APS APM APL FCOS (Tian et al., 2019) ResNeXt-101 44.”
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Asymmetric Loss For Multi-Label Classification
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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.”
BackgroundMethodInfluentialYOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss
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OTA: Optimal Transport Assignment for Object Detection
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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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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.
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