Crafting GBD-Net for Object Detection
Open accessAbstract
The visual cues from multiple support regions of different sizes and resolutions are complementary in classifying a candidate box in object detection. Effective integration of local and contextual visual cues from these regions has become a fundamental problem in object detection. In this paper, we propose a gated bi-directional CNN (GBD-Net) to pass messages among features from different support regions during both feature learning and feature extraction. Such message passing can be implemented through convolution between neighboring support regions in two directions and can be conducted in various layers. Therefore, local and contextual visual patterns can validate the existence of each other by learning their nonlinear relationships and their close interactions are modeled in a more complex way. It is also shown that message passing is not always helpful but dependent on individual samples. Gated functions are therefore needed to control message transmission, whose on-or-offs are controlled by extra visual evidence from the input sample. The effectiveness of GBD-Net is shown through experiments on three object detection datasets, ImageNet, Pascal VOC2007 and Microsoft COCO. Besides the GBD-Net, this paper also shows the details of our approach in winning the ImageNet object detection challenge of 2016, with source code provided on https://github.com/craftGBD/craftGBD. In this winning system, the modified GBD-Net, new pretraining scheme and better region proposal designs are provided. We also show the effectiveness of different network structures and existing techniques for object detection, such as multi-scale testing, left-right flip, bounding box voting, NMS, and context.
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In contrast to the somewhat naive approach of learning CNN features for each region separately and then concatenating them, GBDNet passes messages among features from different contextual regions.
“In contrast to the somewhat naive approach of learning CNN features for each region separately and then concatenating them, GBDNet passes messages among features from different contextual regions.”
From Deep Learning for Generic Object Detection: A Survey (2020) · cites this paper as Zeng et al. 2017
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- Authors
- Zeng, X., Ouyang, W., Yan, J., Li, H., Xiao, T., Wang, K., Liu, Y., Zhou, Y., Yang, B., Wang, Z., Zhou, H., & Wang, X.
- Affiliations
- Chinese University of Hong Kong (HK)
- The University of Sydney (AU)
- ORCID
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2018-09-01
- Volume
- 40
- Issue
- 9
- Pages
- 2109-2123
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W2530966705
- Cited by
- 142
- References
- 54
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 7 · 2023: 14 · 2022: 9 · 2021: 12 · 2020: 24 · 2019: 32 · 2018: 26 · 2017: 13
- FWCI
- 10.78
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- SenseTime Group Limited
- Research Grants Council of Hong Kong (CUHK14213616, CUHK14206114, CUHK14205615, CUHK419412, CUHK14203015, CUHK14207814, CUHK14239816)
- the Hong Kong Innovation and Technology Support Programme (ITS/121/15FX)
- National Natural Science Foundation of China (61371192)
- ONR (N00014-15-1-2356)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- object detection, message passing, feature fusion, region proposal, pre-training, non-maximum suppression
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