HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition

Yan, Zhicheng; Zhang, Hao; Piramuthu, Robinson; Jagadeesh, Vignesh; DeCoste, Dennis; Wei, Di; Yu, Yizhou

doi:10.1109/iccv.2015.314

Cited by 528 publications

(473 citation statements)

References 26 publications

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“…A principally similar work named hierarchical deep CNN (HD-CNN) [50] has studied the effectiveness of a tree-structured CNN ensemble on general object classification problems involving dozens of classes. However, with only a few classes, it is inconvenient to build multi-level class taxonomy, and appending a near-full-sized CNN structure might not be sufficiently cost-efficient considering the size of the problem.…”

Section: Discussionmentioning

confidence: 99%

“…Different from its ancestor AlexNet [71], VGG-M uses small kernels of size 3 × 3 with 1 pixel-sized padding, making them ideal for encoding the local structural differences. After that, the development on CNN have either sought greater depth by shortcut connection [76][77][78] or more miscellaneous structural complexities [21,50,79].…”

Section: The Baseline Network Structure and Extension Styles For Analmentioning

confidence: 99%

“…Structural extension is a common method for network performance enhancement whose underlying intentions focus either on feature space enhancement [41,[48][49][50] or strong prior generation [51,52], and it has been applied to numerous topics, including classification [49,51], tracking [52], edge detection [41], etc. Specifically, only one paper [53] has been found to directly address the class-imbalance issue by combining the feature vectors outputted from a dual arrangement of auto-encoders, where the issue of cost-efficiency has not been emphasized.…”

Section: Introductionmentioning

confidence: 99%

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Cost-Effective Class-Imbalance Aware CNN for Vehicle Localization and Categorization in High Resolution Aerial Images

Zhu

et al. 2017

Remote Sensing

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Joint vehicle localization and categorization in high resolution aerial images can provide useful information for applications such as traffic flow structure analysis. To maintain sufficient features to recognize small-scaled vehicles, a regions with convolutional neural network features (R-CNN) -like detection structure is employed. In this setting, cascaded localization error can be averted by equally treating the negatives and differently typed positives as a multi-class classification task, but the problem of class-imbalance remains. To address this issue, a cost-effective network extension scheme is proposed. In it, the correlated convolution and connection costs during extension are reduced by feature map selection and bi-partite main-side network construction, which are realized with the assistance of a novel feature map class-importance measurement and a new class-imbalance sensitive main-side loss function. By using an image classification dataset established from a set of traditional real-colored aerial images with 0.13 m ground sampling distance which are taken from the height of 1000 m by an imaging system composed of non-metric cameras, the effectiveness of the proposed network extension is verified by comparing with its similarly shaped strong counter-parts. Experiments show an equivalent or better performance, while requiring the least parameter and memory overheads are required.

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Section: Discussionmentioning

confidence: 99%

Section: The Baseline Network Structure and Extension Styles For Analmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cost-Effective Class-Imbalance Aware CNN for Vehicle Localization and Categorization in High Resolution Aerial Images

Zhu

et al. 2017

Remote Sensing

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“…For instance, one can not only build identifiers directly based on attributes [10], but also efficiently construct highly flexible large-scale hierarchical datasets, which can further benefit image classification and attributeto-image generation [23,22].…”

Section: Classi Ermentioning

confidence: 99%

Wavelength overprovisioning strategies for enhanced optical path restoration

Wang

Furdek

Wosinska

et al. 2016

2016 18th International Conference on Transparent Optical Networks (ICTON)

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Predicting attributes from face images in the wild is a challenging computer vision problem. To automatically describe face attributes from face containing images, traditionally one needs to cascade three technical blocks -face localization, facial descriptor construction, and attribute classification -in a pipeline. As a typical classification problem, face attribute prediction has been addressed using deep learning. Current state-of-the-art performance was achieved by using two cascaded Convolutional Neural Networks (CNNs), which were specifically trained to learn face localization and attribute description. In this paper, we experiment with an alternative way of employing the power of deep representations from CNNs. Combining with conventional face localization techniques, we use off-the-shelf architectures trained for face recognition to build facial descriptors. Recognizing that the describable face attributes are diverse, our face descriptors are constructed from different levels of the CNNs for different attributes to best facilitate face attribute prediction. Experiments on two large datasets, LFWA and CelebA, show that our approach is entirely comparable to the state-of-the-art. Our findings not only demonstrate an efficient face attribute prediction approach, but also raise an important question: how to leverage the power of off-the-shelf CNN representations for novel tasks.

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“…For example, Salakhutdinov et al [33] presented a hierarchical classification model to share features between categories, and boosted the classification performance for objects with few training examples. Yan et al [49] presented a hierarchical deep CNN (HD-CNN) that consists of a coarse component trained over all classes as well as several fine components trained over subsets of classes. Instead of utilizing a fixed architecture for classification, Murdock et al [29] proposed a regularization method, i.e.…”

Section: Hierarchical Models For Image Classificationmentioning

confidence: 99%

CNN-RNN: a large-scale hierarchical image classification framework

Guo¹,

Liu²,

Bakker³

et al. 2017

Multimed Tools Appl

120

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Objects are often organized in a semantic hierarchy of categories, where finelevel categories are grouped into coarse-level categories according to their semantic relations. While previous works usually only classify objects into the leaf categories, we argue that generating hierarchical labels can actually describe how the leaf categories evolved from higher level coarse-grained categories, thus can provide a better understanding of the objects. In this paper, we propose to utilize the CNN-RNN framework to address the hierarchical image classification task. CNN allows us to obtain discriminative features for the input images, and RNN enables us to jointly optimize the classification of coarse and fine labels. This framework can not only generate hierarchical labels for images, but also improve the traditional leaf-level classification performance due to incorporating the hierarchical information. Moreover, this framework can be built on top of any CNN architecture which is primarily designed for leaf-level classification. Accordingly, we build a high performance network based on the CNN-RNN paradigm which outperforms the original CNN (wider-ResNet) and also the current state-of-the-art. In addition, we investigate how to utilize the CNN-RNN framework to improve the fine category classification when a fraction of the training data is only annotated with coarse labels. Experimental results demonstrate that CNN-RNN can use the coarse-labeled training data to improve the classification of fine categories, and in some cases it even surpasses the performance achieved by fully annotated training data. This reveals that, CNN-RNN can alleviate the challenge of specialized and expensive annotation of fine labels.

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HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition

Cited by 528 publications

References 26 publications

Cost-Effective Class-Imbalance Aware CNN for Vehicle Localization and Categorization in High Resolution Aerial Images

Cost-Effective Class-Imbalance Aware CNN for Vehicle Localization and Categorization in High Resolution Aerial Images

Wavelength overprovisioning strategies for enhanced optical path restoration

CNN-RNN: a large-scale hierarchical image classification framework

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