Is object localization for free? - Weakly-supervised learning with convolutional neural networks
Open accessAbstract
Successful methods for visual object recognition typically rely on training datasets containing lots of richly annotated images. Detailed image annotation, e.g. by object bounding boxes, however, is both expensive and often subjective. We describe a weakly supervised convolutional neural network (CNN) for object classification that relies only on image-level labels, yet can learn from cluttered scenes containing multiple objects. We quantify its object classification and object location prediction performance on the Pascal VOC 2012 (20 object classes) and the much larger Microsoft COCO (80 object classes) datasets. We find that the network (i) outputs accurate image-level labels, (ii) predicts approximate locations (but not extents) of objects, and (iii) performs comparably to its fully-supervised counterparts using object bounding box annotation for training.
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“Recent MIL based approaches have greatly benefited from using deep neural networks for feature extraction and aggregation (Ilse et al., 2018; Wang et al., 2016; Oquab et al., 2015).”
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- Authors
- Oquab, M., Bottou, L., Laptev, I., & Sivic, J.
- Affiliations
- Institut national de recherche en sciences et technologies du numérique (FR)
- ORCID
- Laptev, Ivan
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 685-694
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1994488211
- Cited by
- 886
- References
- 61
- Citations by year
- 2026: 12 · 2025: 23 · 2024: 33 · 2023: 55 · 2022: 70 · 2021: 104 · 2020: 134 · 2019: 138 · 2018: 126 · 2017: 110 · 2016: 66 · 2015: 12 · 2014: 1 · 2012: 1
- FWCI
- 42.54
- Type
- conference paper
- Language
- en
- Open access
- green
- inria.hal.science
- Licence
- other-oa
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- HAL (submittedVersion, other-oa)
- 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 › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- weakly supervised learning, object localization, object classification, image-level labels, convolutional neural networks, Pascal VOC 2012, cluttered scenes, bounding box annotation, visual object recognition
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