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Cited by 784

The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.

HCP: A Flexible CNN Framework for Multi-Label Image Classification

Open access
Wei, Yunchao, Xia, Wei, Lin, Min, Huang, Junshi, Ni, Bingbing, Dong, Jian, Zhao, Yao, Yan, Shuicheng·2016·IEEE Transactions on Pattern Analysis and Machine Intelligence
DOI 10.1109/tpami.2015.2491929
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Abstract

Convolutional Neural Network (CNN) has demonstrated promising performance in single-label image classification tasks. However, how CNN best copes with multi-label images still remains an open problem, mainly due to the complex underlying object layouts and insufficient multi-label training images. In this work, we propose a flexible deep CNN infrastructure, called Hypotheses-CNN-Pooling (HCP), where an arbitrary number of object segment hypotheses are taken as the inputs, then a shared CNN is connected with each hypothesis, and finally the CNN output results from different hypotheses are aggregated with max pooling to produce the ultimate multi-label predictions. Some unique characteristics of this flexible deep CNN infrastructure include: 1) no ground-truth bounding box information is required for training; 2) the whole HCP infrastructure is robust to possibly noisy and/or redundant hypotheses; 3) the shared CNN is flexible and can be well pre-trained with a large-scale single-label image dataset, e.g., ImageNet; and 4) it may naturally output multi-label prediction results. Experimental results on Pascal VOC 2007 and VOC 2012 multi-label image datasets well demonstrate the superiority of the proposed HCP infrastructure over other state-of-the-arts. In particular, the mAP reaches 90.5% by HCP only and 93.2% after the fusion with our complementary result in [44] based on hand-crafted features on the VOC 2012 dataset.

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Show all 15 references

Research works printed but not linked: 28

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Metadata

Authors
Wei, Y., Xia, W., Lin, M., Huang, J., Ni, B., Dong, J., Zhao, Y., & Yan, S.
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2016-09-01
Volume
38
Issue
9
Pages
1901-1907
ISSN
  • 0162-8828 (ISSN-L)
  • 2160-9292
  • 1939-3539
DOI
10.1109/tpami.2015.2491929
OpenAlex
W1567302070
Cited by
784
References
15
FWCI
65.28
Type
journal article
Language
en
Open access
green
Licence
Funders
  • National Basic Research Program of China (2012CB316400)
  • Fundamental Scientific Research Project (K15JB00360)
  • National NSF of China (61210006, 61532005)

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CoChat. (2026). Citation check: HCP: A Flexible CNN Framework for Multi-Label Image Classification. https://app.cochat.ai/papers/10.1109/tpami.2015.2491929
The paper
Wei, Y., Xia, W., Lin, M., Huang, J., Ni, B., Dong, J., Zhao, Y., & Yan, S. (2016). HCP: A Flexible CNN Framework for Multi-Label Image Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2015.2491929

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