Weakly Supervised Object Localization with Multi-Fold Multiple Instance Learning
Open accessAbstract
Object category localization is a challenging problem in computer vision. Standard supervised training requires bounding box annotations of object instances. This time-consuming annotation process is sidestepped in weakly supervised learning. In this case, the supervised information is restricted to binary labels that indicate the absence/presence of object instances in the image, without their locations. We follow a multiple-instance learning approach that iteratively trains the detector and infers the object locations in the positive training images. Our main contribution is a multi-fold multiple instance learning procedure, which prevents training from prematurely locking onto erroneous object locations. This procedure is particularly important when using high-dimensional representations, such as Fisher vectors and convolutional neural network features. We also propose a window refinement method, which improves the localization accuracy by incorporating an objectness prior. We present a detailed experimental evaluation using the PASCAL VOC 2007 dataset, which verifies the effectiveness of our approach.
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Multi-instance learning is a group of supervised learning algorithms that has seen widespread application in WSOD.
“Multi-instance learning is a group of supervised learning algorithms that has seen widespread application in WSOD [202-209].”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [204]
Recent work has shown that CNNs have a remarkable ability to localize objects in CONV layers, an ability weakened in FC layers.
“Recent work has shown that CNNs have a remarkable ability to localize objects in CONV layers (Zhou et al. 2015(Zhou et al. , 2016a;;Cinbis et al. 2017;Oquab et al. 2015;Hariharan et al. 2016), an 11 ability which is weakened in the FC layers.”
From Deep Learning for Generic Object Detection: A Survey (2020) · cites this paper as Cinbis et al. 2017
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- Authors
- Cinbis, R. G., Verbeek, J., & Schmid, C.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2017-01-01
- Volume
- 39
- Issue
- 1
- Pages
- 189-203
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2133324800
- Cited by
- 420
- References
- 61
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 14 · 2023: 29 · 2022: 42 · 2021: 59 · 2020: 69 · 2019: 78 · 2018: 56 · 2017: 39 · 2016: 23 · 2015: 2
- FWCI
- 44.05
- 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)
- Bilkent University Institutional Repository (Bilkent University) (submittedVersion)
- HAL (submittedVersion, other-oa)
- OpenMETU (Middle East Technical University) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- AXES
- ERC
- ALLEGRO
- LEAR team
- Inria Grenoble Rhône-Alpes
- Laboratoire Jean Kuntzmann
- CNRS
- University Grenoble Alpes, France
- 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
- weakly supervised object localization, multiple instance learning, bounding box annotation, Fisher vectors, convolutional neural network features, Pascal VOC 2007
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