Fusing Context Features and Spatial Attention to Improve Object Detection
Source abstract
Abstract
Context features are mostly used to determine the boundary of a target, which allows one to better locate an object. In this paper, we propose the fusion of the spatial attention mechanism and contextual features to simulate the recognition of objects based on the human eye, thereby improving the detection accuracy of detectors. We chose the PASCAL VOC2007+2012 general dataset to test the generality of our method and examined the improved accuracy of our proposed detector on various targets. Our method showed improved accuracy for small targets and partially overlapping targets. Our proposed model improved the detector’s accuracy by 3.34%.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Visual Attention and Saliency Detection · Advanced Image and Video Retrieval Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, China
Department of Electrical Engineering, University of Chile, Santiago 8370451, Chile
Tianjin Key Laboratory of Advanced Networking (TANK), College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
Funding
China Guangxi Science and Technology Plan Project · National Key R&D Program of China · National Science Foundation of China · Tianjin Intelligent Manufacturing Special Fund Project · Chile CONICYT FONDECYT · Chile CONICYT FONDEF
References (26)
Show source-provided references
1. Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7–12). Faster r-cnn: Towards real-time object detection with region proposal networks. Proceedings of the Annual Conference on Neural Information Processing Systems 2015 (NIPS 2015), Montreal, QC, Canada.
2. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2015). You Only Look Once: Unified Real-Time Object Detection. arXiv.10.1109/CVPR.2016.91 ↗
3. Redmon, J., and Farhadi, A. (2017, January 21–26). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.690 ↗
4. Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv.
5. Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv.
6. Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., and Nie, W. (2022). YOLO-v6: A single-stage object detection framework for industrial applications. arXiv.
7. Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2022). YOLO-v7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv.
8. Re-thinking co-salient object detection
9. Tan, M., Pang, R., and Le, Q.V. (2020, January 14–19). Efficientdet: Scalable and efficient object detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR42600.2020.01079 ↗
10. Law, H., and Deng, J. (2018, January 8–14). Cornernet: Detecting objects as paired keypoints. Proceedings of the European conference on computer vision (ECCV), Munich, Germany.10.1007/978-3-030-01264-9_45 ↗
11. Group object detection and tracking by combining RPCA and fractal analysis10.1007/s00500-016-2329-1 ↗
12. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18–22). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00474 ↗
13. Zhang, Y., Zhang, J., and Guo, X. (2019, January 21–25). Kindling the darkness: A practical low-light image enhancer. Proceedings of the 27th ACM international conference on multimedia, Nice, France.10.1145/3343031.3350926 ↗
14. Low-light image enhancement using Gaussian Process for features retrieval10.1016/j.image.2019.02.001 ↗
15. Kaya, E.C., and Alatan, A.A. (2018, January 7–10). Improving proposal-based object detection using convolutional context features. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.10.1109/ICIP.2018.8451686 ↗
16. Zhu, X., Cheng, D., Zhang, Z., Lin, S., and Dai, J. (2019, January 15–20). An empirical study of spatial attention mechanisms in deep networks. Proceedings of the IEEE/CVF International Conference on Computer Vision, Long Beach, CA, USA.10.1109/ICCV.2019.00679 ↗
17. Zhou, X., Zhuo, J., and Krahenbuhl, P. (2019, January 15–20). Bottom-up object detection by grouping extreme and center points. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.10.1109/CVPR.2019.00094 ↗
18. Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). Yolox: Exceeding yolo series in 2021. arXiv.
19. Foveabox: Beyound anchor-based object detection10.1109/TIP.2020.3002345 ↗
20. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11–14). Ssd: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.10.1007/978-3-319-46448-0_2 ↗
21. Wang, J., Chen, K., Yang, S., Loy, C.C., and Lin, D. (2019, January 15–20). Region proposal by guided anchoring. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.10.1109/CVPR.2019.00308 ↗
22. Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (2019, January 15–20). Searching for mobilenetv3. Proceedings of the IEEE/CVF International Conference on Computer Vision, Long Beach, CA, USA.10.1109/ICCV.2019.00140 ↗
23. Tan, M., and Le, Q. (2019, January 9–15). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning, PMLR, Long Beach, CA, USA.
24. Fan, Q., Fan, D.P., Fu, H., Tang, C.K., Shao, L., and Tai, Y.W. (2021, January 20–25). Group collaborative learning for co-salient object detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.10.1109/CVPR46437.2021.01211 ↗
25. Deep salient object detection with contextual information guidance10.1109/TIP.2019.2930906 ↗
26. Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8–14). Cbam: Convolutional block attention module. Proceedings of the European conference on computer vision (ECCV), Munich, Germany.10.1007/978-3-030-01234-2_1 ↗