Path Aggregation Network for Instance Segmentation
Open accessAbstract
The way that information propagates in neural networks is of great importance. In this paper, we propose Path Aggregation Network (PANet) aiming at boosting information flow in proposal-based instance segmentation framework. Specifically, we enhance the entire feature hierarchy with accurate localization signals in lower layers by bottom-up path augmentation, which shortens the information path between lower layers and topmost feature. We present adaptive feature pooling, which links feature grid and all feature levels to make useful information in each level propagate directly to following proposal subnetworks. A complementary branch capturing different views for each proposal is created to further improve mask prediction. These improvements are simple to implement, with subtle extra computational overhead. Yet they are useful and make our PANet reach the 1st place in the COCO 2017 Challenge Instance Segmentation task and the 2nd place in Object Detection task without large-batch training. PANet is also state-of-the-art on MVD and Cityscapes.
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PANet has a better fusion effect than FPN.
“Therefore, PANet has a better fusion effect than FPN.”
From A deep learning model for steel surface defect detection (2024) · cites this paper as [22]
For fair comparison, original FPN and PANet are repeated multiple times and all convs are replaced with depthwise separable convs, same as BiFPN.
“Notably, the original FPN [23] and PANet [26] only have one top-down or bottom-up flow, but for fair comparison, here we repeat each of them multiple times and replace all convs with depthwise separable convs, which is the same as BiFPN.”
From EfficientDet: Scalable and Efficient Object Detection (2019) · cites this paper as [26]
PANet adopts a bottom-up branch to upload underlying information in fewer residual nodes to enhance the semantic information of low-level feature maps.
“PANet [9] adopts another bottom-up branch to make the underlying information uploaded in fewer residual nodes to enhance the semantic information of low-level feature maps.”
From MME-YOLO: Multi-Sensor Multi-Level Enhanced YOLO for Robust Vehicle Detection in Traffic Surveillance (2020) · cites this paper as [9]
The CSP and PANet neck with an SPP block generates feature pyramids that help the overall network generalize objects of different scales and sizes.
“This neck generates feature pyramids that help the overall network to generalize objects of different scales and sizes.”
From Real-Time Vehicle Classification and Tracking Using a Transfer Learning-Improved Deep Learning Network (2022) · cites this paper as [54]
Using feature pyramids, PANet, and SPP blocks improves object identification on unseen data.
“Using feature pyramids, PANet, and SPP blocks improves object identification on unseen data as well.”
From Real-Time Vehicle Classification and Tracking Using a Transfer Learning-Improved Deep Learning Network (2022) · cites this paper as [54]
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“…to output node if they are at the same level, in order to fuse more features without adding much cost, as shown in Figure 2(f); Third, unlike PANet [19] that only has one top-down and one bottom-up path, we treat each bidirectional (top-down & bottom-up) path as one feature network layer, and…”
BackgroundMethodInfluentialYOLOP: You Only Look Once for Panoptic Driving Perception
Wu, D., Liao, M.-W., Zhang, W.-T., Wang, X.-G., Bai, X., Cheng, W.-Q., & Liu, W.-Y. (2022) · Machine Intelligence Research · doi:10.1007/s11633-022-1339-y · Cited by 466 · see citing works
Dynamic DETR: End-to-End Object Detection with Dynamic Attention
Dai, X., Chen, Y., Yang, J., Zhang, P., Yuan, L., & Zhang, L. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00298 · Cited by 456 · see citing works
A Modified YOLOv8 Detection Network for UAV Aerial Image Recognition
Li, Y., Fan, Q., Huang, H., Han, Z., & Gu, Q. (2023) · Drones · doi:10.3390/drones7050304 · Cited by 415 · see citing works
Yolo-facev2: A scale and occlusion aware face detector
Yu, Z., Huang, H., Chen, W., Su, Y., Liu, Y., & Wang, X. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110714 · Cited by 476 · see citing works
Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification
Xu, A., Yao, A., Li, A., Liang, A., & Zhang, A. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00675 · Cited by 228 · see citing works
Deep Learning for Generic Object Detection: A Survey
Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2018) · arXiv · doi:10.48550/arxiv.1809.02165 · Cited by 2,965 · see citing works
Attention Mask R-CNN for ship detection and segmentation from remote sensing images
Nie, X., Duan, M., Ding, H., Hu, B., & Wong, E. K. (2020) · IEEE Access · doi:10.1109/access.2020.2964540 · Cited by 200 · see citing works
Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation
Chen, L.-C., Lopes, R. G., Cheng, B., Collins, M. D., Cubuk, E. D., Zoph, B., Adam, H., & Shlens, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58545-7_40 · Cited by 165 · see citing works
Traffic flow estimation with data from a video surveillance camera
Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., & Minbaleev, A. (2019) · Journal Of Big Data · doi:10.1186/s40537-019-0234-z · Cited by 149 · see citing works
PAN++: Towards Efficient and Accurate End-to-End Spotting of Arbitrarily-Shaped Text
Wang, W., Xie, E., Li, X., Liu, X., Liang, D., Zhibo, Y., Lu, T., & Shen, C. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3077555 · Cited by 140 · see citing works
DC-YOLOv8: Small Size Object Detection Algorithm Based on Camera Sensor
Lou, H., Duan, X., Guo, J., Liu, H., Gu, J., Bi, L., & Chen, H. (2023) · doi:10.20944/preprints202304.0124.v1 · Cited by 638 · see citing works
T-YOLO: Tiny Vehicle Detection Based on YOLO and Multi-Scale Convolutional Neural Networks
Padilla Carrasco, D., Rashwan, H. A., García, M. Á., & Puig, D. (2023) · IEEE Access · doi:10.1109/access.2021.3137638 · Cited by 135 · see citing works
Dense and Small Object Detection in UAV Vision Based on Cascade Network
Zhang, X., Izquierdo, E., & Chandramouli, K. (2019) · 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2019.00020 · Cited by 129 · see citing works
A deep learning model for steel surface defect detection
Li, Z., Wei, X., Hassaballah, M., Li, Y., & Jiang, X. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01180-7 · Cited by 122 · see citing works
“The neck network is the PANet [22] structure, and the head network is the YOLO head [15].”
PP-PicoDet: A Better Real-Time Object Detector on Mobile Devices
Yu, G., Chang, Q., Lv, W., Xu, C., Cui, C., Ji, W., Dang, Q., Deng, K., Wang, G., Du, Y., et al. (2021) · arXiv · doi:10.48550/arxiv.2111.00902 · Cited by 112 · see citing works
“CSP-PAN.”
MethodInfluentialHit-Detector: Hierarchical Trinity Architecture Search for Object Detection
Guo, J., Han, K., Wang, Y., Zhang, C., Yang, Z., Wu, H., Chen, X., & Xu, C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01142 · Cited by 110 · see citing works
A vehicle detection method based on disparity segmentation
Li, S., Chen, J., Peng, W., Shi, X., & Bu, W. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14360-x · Cited by 102 · see citing works
Neupane, B., Horanont, T., & Aryal, J. (2022) · Sensors · doi:10.3390/s22103813 · Cited by 85 · see citing works
“For the neck of YOLOv5, we used the CSP and a path aggregation network (PANet) [54] with a spatial pyramid pooling (SPP) block [55].”
MethodSP-NAS: Serial-to-Parallel Backbone Search for Object Detection
Jiang, C., Xu, H., Zhang, W., Liang, X., & Li, Z. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01188 · Cited by 67 · see citing works
Zhu, J., Li, X., Jin, P., Xu, Q., Sun, Z., & Song, X. (2020) · Sensors · doi:10.3390/s21010027 · Cited by 51 · see citing works
“PANet [9] adopts another bottom-up branch to make the underlying information uploaded in fewer residual nodes to enhance the semantic information of low-level feature maps.”
BackgroundShamsolmoali, P., Chanussot, J., Zareapoor, M., Zhou, H., & Yang, J. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3106442 · Cited by 44 · see citing works
Bai, Y., Zha, B., Sezen, H., & Yilmaz, A. (2023) · Structural Health Monitoring · doi:10.1177/14759217221083649 · Cited by 42 · see citing works
Qiao, S., Pang, S., Luo, G., Pan, S., Chen, T., & Lv, Z. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3091579 · Cited by 41 · see citing works
Zhang, L., Dong, D., Sun, Y., Hu, C., Sun, C., Wu, Q., & Tian, J. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.17854 · Cited by 42 · see citing works
PVSPE: A pyramid vision multitask transformer network for spacecraft pose estimation
Yang, H., Xiao, X., Yao, M., Xiong, Y., Cui, H., & Fu, Y. (2024) · Advances in Space Research · doi:10.1016/j.asr.2024.05.011 · Cited by 41 · see citing works
Overview of single-stage object detection models: from Yolov1 to Yolov7
Yasmine, G., Maha, G., & Hicham, M. (2023) · 2023 International Wireless Communications and Mobile Computing (IWCMC) · doi:10.1109/iwcmc58020.2023.10182423 · Cited by 37 · see citing works
ISDNet: AI-enabled Instance Segmentation of Aerial Scenes for Smart Cities
Garg, P., Chakravarthy, A. S., Mandal, M., Narang, P., Chamola, V., & Guizani, M. (2021) · ACM Transactions on Internet Technology · doi:10.1145/3418205 · Cited by 32 · see citing works
End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales
Bai, Y., Sezen, H., & Yilmaz, A. (2021) · 2020 25th International Conference on Pattern Recognition (ICPR) · doi:10.1109/icpr48806.2021.9413041 · Cited by 32 · see citing works
Outdoor RGBD Instance Segmentation With Residual Regretting Learning
Xu, Z., Liu, S., Shi, J., & Lu, C. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2975711 · Cited by 20 · see citing works
Self-citationImproving single-stage object detectors for nighttime pedestrian detection
Devi, S., Thopalli, K., Malarvezhi, P., & Thiagarajan, J. J. (2022) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001422500343 · Cited by 15 · see citing works
Spacecraft Homography Pose Estimation with Single-Stage Deep Convolutional Neural Network
Chen, S., Yang, W., Wang, W., Mai, J., Liang, J., & Zhang, X. (2024) · Sensors · doi:10.3390/s24061828 · Cited by 12 · see citing works
- Authors
- Liu, S., Qi, L., Qin, H., Shi, J., & Jia, J.
- Affiliations
- Chinese University of Hong Kong (HK)
- Peking University (CN)
- Sensetime (China) (CN)
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 8759-8768
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963857746
- Semantic Scholar
- 5cc22f65bf4e5c0aa61f3139da832d3a946e15cf
- Cited by
- 10,011
- References
- 70
- Citations by year
- 2026: 1200 · 2025: 1866 · 2024: 2087 · 2023: 1882 · 2022: 1478 · 2021: 928 · 2020: 361 · 2019: 182 · 2018: 27
- FWCI
- 112.88
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- arXiv (Cornell University)
- Funders
- SenseTime Group
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- path aggregation network, instance segmentation, information flow, feature hierarchy, mask prediction, multi-view feature fusion, object detection
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