# ASF-YOLO: A Novel YOLO Model with Attentional Scale Sequence Fusion for Cell Instance Segmentation

arXiv: https://arxiv.org/abs/2312.06458 · HTML: https://mlsanity.com/paper/2312.06458/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 60 references that are in the map.
- First posted on arXiv 11 Dec 2023.

## Builds on (most cited first, 22 of 22)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning](https://mlsanity.com/paper/1702.03118/) (2017)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://mlsanity.com/paper/1911.08287/) (2019)
- [YOLACT: Real-time Instance Segmentation](https://mlsanity.com/paper/1904.02689/) (2019)
- [Soft-NMS -- Improving Object Detection With One Line of Code](https://mlsanity.com/paper/1704.04503/) (2017)
- [CSPNet: A New Backbone that can Enhance Learning Capability of CNN](https://mlsanity.com/paper/1911.11929/) (2019)
- [SOLO: Segmenting Objects by Locations](https://mlsanity.com/paper/1912.04488/) (2019)
- [Coordinate Attention for Efficient Mobile Network Design](https://mlsanity.com/paper/2103.02907/) (2021)
- [Focal and Efficient IOU Loss for Accurate Bounding Box Regression](https://mlsanity.com/paper/2101.08158/) (2021)
- [Adapting Mask-RCNN for Automatic Nucleus Segmentation](https://mlsanity.com/paper/1805.00500/) (2018)
- [ssFPN: Scale Sequence (S^2) Feature Based-Feature Pyramid Network for Object Detection](https://mlsanity.com/paper/2208.11533/) (2022)

## Built on by (most cited first, 11 of 11)

- [YOLO26: Key Architectural Enhancements and Performance Benchmarking for Real-Time Object Detection](https://mlsanity.com/paper/2509.25164/) (2025)
- [Real-Time Detection for Small UAVs: Combining YOLO and Multi-frame Motion Analysis](https://mlsanity.com/paper/2411.02582/) (2024)
- [A Simple Detector with Frame Dynamics is a Strong Tracker](https://mlsanity.com/paper/2505.04917/) (2025)
- [DASSF: Dynamic-Attention Scale-Sequence Fusion for Aerial Object Detection](https://mlsanity.com/paper/2406.12285/) (2024)
- [SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery](https://mlsanity.com/paper/2507.12727/) (2025)
- [M^3-GloDets: Multi-Region and Multi-Scale Analysis of Fine-Grained Diseased Glomerular Detection](https://mlsanity.com/paper/2508.17666/) (2025)
- [DEAL-YOLO: Drone-based Efficient Animal Localization using YOLO](https://mlsanity.com/paper/2503.04698/) (2025)
- [YOLO-FireAD: Efficient Fire Detection via Attention-Guided Inverted Residual Learning and Dual-Pooling Feature Preservation](https://mlsanity.com/paper/2505.20884/) (2025)
- [IrisNet: Infrared Image Status Awareness Meta Decoder for Infrared Small Targets Detection](https://mlsanity.com/paper/2511.20319/) (2025)
- [PLAS-Net: Pixel-Level Area Segmentation for UAV-Based Beach Litter Monitoring](https://mlsanity.com/paper/2604.21313/) (2026)
- [A Camera-Cooperative ISAC Framework for Multimodal Non-Cooperative UAVs Sensing](https://mlsanity.com/paper/2605.22090/) (2026)

## Abstract (arXiv)

We propose a novel Attentional Scale Sequence Fusion based You Only Look Once (YOLO) framework (ASF-YOLO) which combines spatial and scale features for accurate and fast cell instance segmentation. Built on the YOLO segmentation framework, we employ the Scale Sequence Feature Fusion (SSFF) module to enhance the multi-scale information extraction capability of the network, and the Triple Feature Encoder (TFE) module to fuse feature maps of different scales to increase detailed information. We further introduce a Channel and Position Attention Mechanism (CPAM) to integrate both the SSFF and TPE modules, which focus on informative channels and spatial position-related small objects for improved detection and segmentation performance. Experimental validations on two cell datasets show remarkable segmentation accuracy and speed of the proposed ASF-YOLO model. It achieves a box mAP of 0.91, mask mAP of 0.887, and an inference speed of 47.3 FPS on the 2018 Data Science Bowl dataset, outperforming the state-of-the-art methods. The source code is available at https://github.com/mkang315/ASF-YOLO.